Methods and systems for determining an algorithm setting based on a skew metric

ABSTRACT

A physiological monitoring system may determine physiological information, such as physiological rate information, from a physiological signal. The system may determine a skew metric based on the physiological signal. The system may determine an algorithm setting based on a reference relationship between the determined skew metric and a value indicative of a physiological rate. The algorithm setting may, for example, affect the amount of filtering applied to the physiological signal.

The present disclosure relates to determining physiological information.More particularly, the present disclosure relates to determining analgorithm setting based on a skew metric.

SUMMARY

A physiological monitor may determine one or more physiologicalparameters such as, for example, a physiological rate based on signalsreceived from one or more physiological sensors. The physiologicalmonitor may analyze physiological signals (e.g., photoplethysmographic(PPG) signals) for oscillometric behavior characterized by a pulse rate,a respiration rate, or both. Physiological signals may include one ormore noise components, which may include the effects of ambient light,electromagnetic radiation from powered devices (e.g., at 60 Hz or 50HZ), subject movement, and/or other non-physiological or undesiredphysiological signal components. In some circumstances, thedetermination of a pulse rate from photoplethysmographic information maypresent challenges. Factors such as, for example, noise, subjectmovement, the shape of physiological pulses, subjects having lowperfusion with small physiological pulses, and/or other factors maypresent challenges in determining pulse rate.

The physiological monitor in accordance with the present disclosure mayinclude a sensor, having a detector, which may generate an intensitysignal (e.g., a PPG signal) based on light attenuated by the subject.Processing equipment of the monitor, a processing module, and/or othersuitable processing equipment may determine a value indicative of aphysiological rate based on the intensity signal. For example, theprocessing equipment may determine a pulse rate based on analysis of theintensity signal.

In some embodiments, the processing equipment may use one or moreoperating modes to determine a physiological parameter of a subject. Anoperating mode may be used to determine the physiological parameter(e.g., pulse rate) based on a particular criterion. A first mode mayuse, for example, relatively strict criteria to determine thephysiological parameter, but may not be required to post a rate asoutput. Another mode may implement a relatively narrow, adjustableband-pass filter on the physiological data (e.g., time series data),which is good at rejecting noise when it is tuned to the correct rate.The various modes may include qualification techniques to qualifycalculated values and physiological parameters determined thereof. Thequalification techniques can provide an accurate assessment of whether acalculated parameter is indicative of a physiological parameter, and canalso prevent the band-pass filter from being initially tuned to noiseand deviating away from the correct physiological rate. In addition, inthe event that the band-pass is tuned incorrectly, the processingequipment may drop out of a current operating mode and return to aninitialization mode based on qualification failure information.Accordingly, physiological parameters (e.g., pulse rate) may be reliablydetermined in the presence of noise.

In some embodiments, the processing equipment may determine one or morealgorithm settings based on the intensity signal, and determine a valueindicative of a physiological rate of the subject based on the intensitysignal and based on the algorithm setting. Determining the valueindicative of the physiological rate may include performing acorrelation calculation such as a correlation, and/or filtering a signal(e.g., a signal derived from an intensity signal) based on the algorithmsetting. The filtering may include applying a bandpass filter, a finiteimpulse response filter, or any other suitable filter, based on thealgorithm settings. In some embodiments, the processing equipment maygenerate a correlation sequence based on segments of the physiologicalsignal to determine a likely period of a periodic physiological behavior(e.g., heart rate). For example, the processing equipment may generatethe correlation sequence, determine a peak in the sequence having a lagvalue (e.g., relative lag in sample number or time between correlatedsegments) indicative of a period, and then qualify the lag value basedon the algorithm settings. Algorithm settings may also be used to applysignal conditioning to the physiological signal prior to processing.

In some embodiments, the processing equipment may fill a buffer withgenerated values when a full window of data is not available. In someembodiments, the processing equipment may determine one or moreinitialization values based on the physiological data, and generate awindow of data based on the one or more initialization values and one ormore samples of the physiological data. The initialization values may bebased on random numbers, values based on noise values, a sample value, ascaled sample value (e.g., scaled by signal noise), any other values, orany combination thereof.

In some embodiments, the processing equipment manages one or more statusflags such as a gain change status flag. In some embodiments, a statusindicator (e.g., a gain change indicator or high noise indicator) may bereceived while the physiological data is being received. For example, again change indicator may be a change in gain in an analog amplifier.The processing equipment may set a period of time during which receivedvalues of the physiological data are not added to the window of data inresponse to receiving the status indicator. The period of time may be,for example, a multiple of a period associated with a physiological rateof the subject. Artifacts associated with, for example, a gain changemay be mitigated by omitting that data from the buffer. In someembodiments, the processing equipment may use a window of data havingvalues taken before and after the period of time. In some suchembodiments, the processing equipment may modify some of the values inthe window of data to smooth the transition between values of thephysiological data received prior to receiving the status indicator andvalues of the physiological data received after the period of time isover.

In some embodiments, the processing equipment may determine one or moremetrics based on the physiological data, which may then be used toclassify the data, determine an algorithm setting, or both. In someembodiments, the algorithm setting affects the amount of filteringapplied to the physiological data. In some embodiments, the processingequipment may generate a difference signal, generate a sorted differencesignal (e.g., sorted by value), partition a window of data, perform anyother data manipulation or calculation, or any combination thereof. Insome embodiments, the processing equipment may monitor the temporalhistory of a metric, and adjust an algorithm setting if the metricexhibits temporal changes greater than a threshold. Some techniques fordetermining algorithm settings are discussed below.

In some embodiments, the processing equipment may generate a sorteddifference signal, and identify two midpoints of two respected segmentsof the sorted difference signal. The processing equipment may determinea first offset associated with one segment (e.g., a value of the segmentat a particular location), and determine a second offset associated withthe other segment (e.g., a value of the segment at a particularlocation). In some embodiments, the processing equipment may determine afirst difference between the first midpoint and the first offset, anddetermine a second difference between the second midpoint and the secondoffset. The processing equipment may determine a ratio based on thefirst difference and the second difference, which may be used toclassify the physiological data, determine an algorithm setting, orboth. In some embodiments, the algorithm setting affects the amount offiltering applied to the physiological data. In some embodiments, thefirst segment may include the positive values and the second segment mayinclude the negative values of the sorted difference signal.

In some embodiments, the processing equipment may generate a differencesignal based on the physiological signal. The processing equipment maydetermine positive areas associated with positive regions of thedifference signal, and determine negative areas associated with negativeregions of the difference signal. The processing equipment may thenclassify the physiological data, determine an algorithm setting, orboth, based on area ratios of adjacent positive and negative areas ofthe difference signal. In some embodiments, the algorithm settingaffects the amount of filtering applied to the physiological data. Thearea ratios may be sorted and one or more ratios may be identified. Forexample, the area ratios may be normalized and the processing equipmentmay identify approximately the twenty-fifth percentile normalized arearatio, which may be indicative of whether the data exhibits a dicroticnotch.

In some embodiments, the processing equipment may generate a sorteddifference signal based on the physiological signal, and then generate asecond difference signal based on the first sorted difference signal. Insome embodiments, the second difference signal may be generated based onthe negative values of the sorted difference signal. The processingequipment may analyze a first segment and a second segment of the seconddifference signal. For example, the first segment may be in the lefthalf of the second difference signal and the second segment may be inthe right half of the second difference signal. The analysis mayinclude, for example, determining a mean value of the first segment anddetermining a mean value of the second segment, and then determining aratio of the mean values.

In some embodiments, the processing equipment may determining a skewmetric based on the physiological signal, and determine an algorithmsetting based on a reference relationship between the determined skewmetric and a value indicative of a physiological rate. The referencerelationship may include, for example, a look-up table, and theprocessing equipment may reference the look-up table using the skewmetric as an input, and determine the value indicative of physiologicalrate based on the look-up table. In a further example, the referencerelationship may include a function, and the processing equipment mayreference the function using the skew metric as an input, and determinethe value indicative of physiological rate based on the function. Insome embodiments, the processing equipment may determine whether toapply a filter, such as a finite impulse response filter, based on theskew metric. In some embodiments, the processing equipment may determinethe central frequency of a band pass filter that may be applied to thedata to be substantially equal to the value indicative of thephysiological rate. In some embodiments, the processing equipment maydetermine the cutoff frequency of a lowpass filter or highpass filter,based on the value indicative of the physiological rate.

In some embodiments, the processing equipment may determine one or morealgorithm settings, and adjust the one or more algorithm settings inresponse to one or more metric values. For example, the processingequipment may receive physiological data, and determine a metric valueindicative of a physiological classification. The physiologicalclassification may be based on the presence of a dicrotic notch,magnitude of a calculated physiological rate, pulse shape, anyphysiological classification, or any combination thereof. In someinstances, subsequent to determining the algorithm setting, theprocessing equipment may determine a second metric value indicative of adifferent physiological classification than determined previously basedon subsequent physiological data. The second metric value may be thesame metric above having an updated value, or a different metric, whichindicates the different physiological classification. The processingequipment may then determine an algorithm setting based on the differentphysiological classification. Accordingly, the processing equipment mayupdate algorithm settings as changes occur in the physiological data, inthe state of the rate algorithm, or both.

In some embodiments, the processing equipment may generate a sorteddifference signal based on the physiological signal, and identify one ormore data points at one or both ends of the sorted difference signal asbeing associated with noise. The processing equipment may determine avalue indicative of noise based on the one or more identified datapoints. In some embodiments, the processing equipment may fit a line toa segment of the sorted difference signal, determine at least onethreshold based on the line fit, and identify the one or more datapoints associated with noise as lying outside of the at least onethreshold. The segment may include samples of the sorted differencesignal having similar slopes. In some embodiments, the processingequipment may fit multiple lines to multiple segments of the sorteddifference signal, determine multiple thresholds based on the line fits,and identify the one or more data points comprises identifying one ormore data points that are outside of the at least one threshold. In somesuch embodiments, the processing equipment may identify the one or moredata points by determining when a difference between two adjacent datapoints in the sorted difference signal is greater than a threshold. Insome such embodiments, the processing equipment may determine the valueindicative of noise based on a ratio of the number of data pointsassociated with noise and a total number of data points in the sorteddifference signal.

In some embodiments, the processing equipment may determine a valueindicative of noise using multiple segments of a sorted differencesignal. The processing equipment may identify a first end group of themultiple segments and a second end group of the multiple segments atopposite ends of the sorted difference signal. The processing equipmentmay then determine at least one threshold based on the first end groupand the second end group, and identify one or more data points of one orboth ends of the sorted difference signal as being associated withnoise. The processing equipment may determine a number of data points atone or both ends of the sorted difference signal as being associatedwith noise, and determine a value indicative of noise based on the oneor more data points. In some embodiments, the processing equipment maydetermine a ratio of the number of number of data points associated withnoise and a total number of points of the physiological signal. Theprocessing equipment may determine an algorithm setting based on thevalue indicative of noise.

In some embodiments, the processing equipment may generate a firstsorted difference signal based on a first segment of the physiologicalsignal, and generate a second sorted difference signal based on a secondsegment of the physiological signal. The processing equipment mayanalyze the first sorted difference signal and the second sorteddifference signal, and determine a value indicative of noise based onthe analysis of the first sorted difference signal and the second sorteddifference signal. In some embodiments, the processing equipment maydetermine a first line fit having a first slope for the first sorteddifference signal, determine a second line fit having a second slope forthe second sorted difference signal, and determine a difference betweenthe first slope and the second slope. In some embodiments, theprocessing equipment may determine a first line fit for the first sorteddifference signal, determine a second line fit for the second sorteddifference signal, determine a goodness of fit associated with the firstline fit, and determine a goodness of fit associated with the secondline fit. In some embodiments, the processing equipment may determine agoodness of fit between the first sorted difference signal and thesecond sorted difference signal. In some embodiments, the processingequipment may determine a value indicative of differences between thefirst sorted difference signal and the second sorted difference signal.

In some embodiments, the processing equipment may generate at least onesorted difference signal based on the physiological signal, analyze theat least one sorted difference signal to determine at least two valuesindicative of noise, and determine a value indicative of asignal-to-noise ratio based on the two or more values indicative ofnoise. In some embodiments, the processing equipment may use a referenceof associated values indicative of noise and values indicative ofsignal-to-noise ratios. In some embodiments, the processing equipmentmay analyze the at least one sorted difference signal to determine threevalues indicative of noise, select the maximum value of the threevalues, and determine the signal-to-noise ratio based on the maximumvalue. In some embodiments, the processing equipment may determine abest fit line based on the at least one sorted difference signal,generate at least one threshold based on the best fit line, anddetermine a number of points of the sorted difference signal thatexceeds the at least one threshold. At least one value of the at leasttwo values indicative of noise may be based on the number of points. Insome embodiments, the processing equipment may generate at least onesecond sorted difference signal based on the physiological signal,determine a first best fit line based on the sorted difference signal,determine a second best fit line based on the second sorted differencesignal, and compare the first best fit line and the second best fit lineto determine at least one value of the at least two values indicative ofnoise.

In some embodiments, the processing equipment may apply signalconditioning to received physiological data. Signal conditioning mayinclude filtering, normalization, baseline subtraction, derivativelimiting, any other suitable signal conditioning, or any combinationthereof. The following discussion provides further details regardingsignal conditioning techniques.

In some embodiments, the processing equipment may calculate differencesbased on the physiological data, identify one or more differences thatexceed a threshold, and modify the physiological data based on the oneor more differences. In some embodiments, the processing equipment maydetermine the threshold based on the differences. In some embodiments,the processing equipment may perform a standard deviation calculationbased on the differences, and determine the threshold based on thestandard deviation calculation. In some embodiments, one of the one ormore differences may be calculated based on two adjacent values, and thephysiological data may be modified by reducing a difference between thetwo adjacent values. Reducing the difference between the two adjacentvalues may include determining an offset based on the two adjacentvalues, and the offset may be applied to one of the two adjacent values.In some embodiments, the processing equipment may determine one or moreoffsets based on the one or more differences, and perform subtractionsbased on the one or more offsets to modify the data.

In some embodiments, the processing equipment may generate a firstsignal based on a stability function such as, a Lyapunov function forexample, applied to the physiological signal, generate a differencesignal based on the first signal, and generate a modified physiologicalsignal based on the difference signal. The processing equipment mayidentify one or more points of the difference signal that exceed athreshold, and generate the modified physiological signal by modifyingone or more points in the physiological signal that correspond to theone or more points of the difference signal. In some embodiments, theprocessing equipment may determine a standard deviation of thedifference signal, and the threshold may be based on the standarddeviation of the difference signal. In some embodiments, the thresholdmay be a predetermined value. In some embodiments, the processingequipment may modify the physiological signal by removing portions ofthe physiological signal associated with the one or more points of thedifference signal that exceed the threshold. In some embodiments, theprocessing equipment may identify the one or more points of thedifference signal that exceed the threshold by identifying a pair of twothreshold crossings and intermediate points between the two thresholdcrossings of the difference signal.

In some embodiments, the processing equipment may generate a sorteddifference signal based on the physiological signal, identify one ormore points in the sorted difference signal that exceed a threshold, andgenerate a modified physiological signal based on the one or morepoints. In some embodiments, the processing equipment may determine thethreshold based on the sorted difference signal. In some embodiments,the processing equipment may determine a line fit based on the sorteddifference signal, and determine the threshold based on the line fit. Insome embodiments, the processing equipment may modify the one or morepoints in the sorted difference signal to generate a modified sorteddifference signal, and generate a modified physiological signal byreordering the modified sorted difference signal to generate a reorderedsignal, and integrating the reordered signal. In some embodiments, theprocessing equipment may modify the one or more points in the sorteddifference signal to be equal to the threshold.

In some embodiments, the processing equipment may generate a positivesignal based on positive values of the physiological signal and generatea negative signal based on negative values of the physiological signal.The processing equipment may filter the positive signal to generate afiltered positive signal and filter the negative signal to generate afiltered negative signal. The processing equipment may combine thefiltered positive signal and the filtered negative signal to generate acombined signal, and modify the physiological signal based on thecombined signal. In some embodiments, the processing equipment maygenerate the positive signal by extracting the positive values of thephysiological signal and inserting zeros corresponding to the negativevalues of the physiological signal, and generate the negative signal byextracting the negative values of the physiological signal and insertingzeros corresponding to the positive values of the physiological signal.Filtering the positive signal and the negative signal may include lowpass filtering the respective signals. In some embodiments, combiningthe filtered positive signal and the filtered negative signal mayinclude averaging the filtered positive signal and the filtered negativesignal. In some embodiments, the processing equipment may scale thecombined signal prior to modifying the physiological signal based on thecombined signal. Scaling the combined signal may include, for example,scaling the combined signal based on a standard deviation of thecombined signal. In some embodiments, the processing equipment maymodify the physiological signal by subtracting the combined signal fromthe physiological signal.

In some embodiments, the processing equipment may calculate absolutevalues based on the physiological signal, which may include positive andnegative values, filter the absolute values to generate a filteredsignal, and modify the physiological signal based on the filteredsignal. Filtering the absolute values may include, for example, low passfiltering the absolute values. In some embodiments, the processingequipment may modify the filtered signal prior to modifying thephysiological signal based on the filtered signal. In some embodiments,modifying the filtered signal may include, for example, performing asubtraction of a minimum value of the filtered signal from the filteredsignal. In some embodiments, modifying the filtered signal may include,for example, adding a gain value to the filtered signal. In someembodiments, modifying the physiological signal may include, forexample, dividing the physiological signal by the filtered signal.

In some embodiments, the processing equipment may determine a metricbased on the physiological signal, and selectively apply, based on themetric, a digital filter to the physiological signal to generate afiltered signal based on two or more filter coefficients. The filteredsignal may correspond to a weighted sum of the physiological signal anda difference signal corresponding to the physiological signal. Forexample, the digital filter may include a finite impulse responsefilter. In some embodiments, the metric may include a de-trending metricindicative of the likely magnitude of a physiological parameter, wherethe two or more filter coefficients may be adjusted based on thede-trending metric. The two or more coefficients may be adjusted toincrease the weight of the physiological signal relative to thedifference signal if the de-trending metric is below a threshold, andmay be adjusted to increase the weight of the difference signal relativeto the physiological signal if the de-trending metric is above athreshold. In some embodiments, the metric may be indicative of thepresence of a dicrotic notch in the physiological signal. In someembodiments, the processing equipment may receive a calculated valueindicative of a physiological rate of the subject, and the two or morefilter coefficients may be adjustable based on a calculated value.

In some embodiments, the processing equipment may determine a valueindicative of a physiological rate based on the physiological signal,determine a metric based on the physiological signal, select one or morebandpass filter settings of a bandpass filter based on the physiologicalrate and based on the metric, and apply the bandpass filter having theselected settings to the physiological signal to generate a filteredsignal. In some embodiments, the metric may be indicative of noise inthe physiological signal. The one or more bandpass filter settings mayinclude, for example, a spectral band, and the one or more selectedbandpass filter settings may include a narrow spectral band when themetric exceeds a threshold. The one or more bandpass filter settings mayinclude, for example, a spectral band, and the one or more selectedbandpass filter settings may include a wide spectral band when themetric is below a threshold. In some embodiments, the one or morebandpass filter settings may include a center frequency that correspondsto the physiological rate.

In some embodiments, the processing equipment may perform a correlationcalculation to analyze periodic components of a physiological signalsuch as, for example, the component attributable to a physiologicalrate. The correlation calculation may include, for example, generating acorrelation sequence, generating a correlation matrix, performing astatistical regression analysis, or a combination thereof.

In some embodiments, the processing equipment may calculate acorrelation sequence corresponding to multiple lag values based on thephysiological signal. For at least one lag value of the multiple lagvalues, the processing equipment may compare the correlation sequencevalue corresponding to the at least one lag value to a threshold. Thethreshold may be predetermined, and may vary as a function of lag. Forexample, the threshold may be a predetermined square root function oflag value. The processing equipment may determine whether thecorrelation sequence value exceeds the threshold, determine whether thecorrelation sequence value corresponds to a peak, and identify aparticular lag value of the multiple lag values when the correlationsequence value corresponding to the particular lag value exceeds thethreshold and corresponds to a peak. In some embodiments, the processingequipment may sequentially compare the correlation sequence valuescorresponding to multiple lag values to the threshold in ascending orderof lag value until the particular lag value is identified. Calculatingthe correlation sequence may include, for example, calculating one valueof the correlation sequence at a time, where the most current lag valueof the multiple lag values is analyzed. In some embodiments, theprocessing equipment may determine whether the correlation sequencevalue corresponds to a peak by determining that correlation sequencevalues adjacent to the correlation sequence value and corresponding tosmaller lag values are increasing with lag value, and that correlationsequence values adjacent to the correlation sequence value andcorresponding to larger lag values are decreasing with lag value. Insome embodiments, the processing equipment may normalize the correlationsequence values, a portion of the physiological signal, or both.

In some embodiments, the processing equipment may generate a lag matrixincluding multiple segments of the physiological signal. Each of themultiple segments may include the same number of samples of thephysiological signal. The processing equipment may generate acorrelation matrix, which includes a set of correlation values, based onthe lag matrix, and identify one or more lag values based on thecorrelation matrix. The correlation matrix may be generated, forexample, by performing a matrix multiplication of the lag matrix with atranspose of the lag matrix. In some embodiments, the processingequipment may process the correlation matrix to generate a processedcorrelation matrix, and identify at least one peak based on theprocessed correlation matrix. For example, the processing equipment mayapply a matrix rotation to the correlation matrix, average correlationvalues along a direction of the correlation matrix to generate an arrayof averaged correlation values, and identify a peak in the array of theaveraged correlation values. The matrix rotation may be, for example, asubstantially 45 degree matrix rotation to align peak values along a rowor column of the correlation matrix. In some embodiments, the processingequipment may identify one or more regions of the correlation matrixcorresponding to noise, and avoid identifying a correlation lag value inthose regions.

In some embodiments, the processing equipment may select a first segmentand a second segment of the physiological signal comprising a pluralityof values, in which the second segment is shifted in time from the firstsegment. The processing equipment may determine a correlation valuebetween the first segment and the second segment, analyze the firstsegment and the second segment to determine a metric, and determinecorrelation information based on the correlation value and based on themetric. Determining the correlation information may include, forexample, modifying the correlation value based on the metric. In someembodiments, the processing equipment may select multiple segments ofthe physiological signal each shifted in time from the first segment bya unique lag, determine multiple correlation values between the firstsegment and the multiple segments, and determine that the correlationvalue corresponds to a peak in the multiple correlation values. In someembodiments, the processing equipment may generate multiple value pairseach including a value of the first segment and a corresponding value ofthe second segment. The processing equipment may determine a metricbased on the plurality of value pairs and a reference relationship. Forexample, the processing equipment may apply a transform to the valuepairs to generate transformed value pairs that include a first value anda second value, and analyze the distribution across the plurality offirst values of each transformed value pairs. In some embodiments, themetric may be a confidence value, and the processing equipment maygenerate a modified confidence value by multiplying the correlationvalue and the metric.

In some embodiments, the processing equipment may qualify or disqualifya calculated value such as, for example, a correlation lag valueassociated with a peak. One or more qualification tests may be appliedto the physiological signal, or processed data arising thereof, todetermine if the value is qualified or not. In some embodiments, theprocessing equipment may use a calculated value in determining whetherthe physiological signal is qualified.

In some embodiments, the processing equipment may select pairs of samplepoints of the physiological signal spaced by a particular spacing basedon the calculated value, determine a state for each of the pairs basedon a set of criteria, determine a number of state transitions based onthe determined states, and qualify or disqualify the calculated valuebased on the number of state transitions. The set of criteria mayinclude, for example, a relative magnitude of sample points in a pair toeach other, a relative magnitude of a sample point to a product of theother sample point in a pair and a coefficient, and a sign of eachsample point in a pair. In some embodiments, the processing equipmentmay compare the number of state transitions to one or more thresholdswhich may include, for example, an upper threshold and a lowerthreshold. The number of states from which the state may be determinedmay be equal to a power of two greater than or equal to 2. In someembodiments, the calculated value may be a correlation lag valuecorresponding to a peak, and the particular spacing is substantially onequarter of the calculated value.

In some embodiments, the processing equipment may determine a skewmetric value based on the physiological signal, determine a correlationlag value corresponding to a peak in a correlation sequence, andqualifying or disqualifying the correlation lag value based on the skewmetric. In some embodiments, the processing equipment may compare theskew metric and the correlation lag value to a reference set of skewmetric values and correlation lag values. For example, the reference setof skew metric values and correlation lag values may be arranged in alook-up table. In a further example, the processing equipment mayidentify a particular skew value of the reference set that most closelymatches the determined skew metric value, determine a difference valueindicative of a difference between the determined correlation lag valeand the correlation lag value of the reference set corresponding to theparticular skew value, and compare the difference value to a threshold.The threshold may be, for example, predetermined or based on thedetermined correlation lag value.

In some embodiments, the processing equipment may determine a differencevalue between a set of sample points and a corresponding set of samplepoints of the physiological signal each spaced apart by a particularspacing based on a calculated value. The processing equipment maycompute a sum based on the determined difference values, and qualify ordisqualify the calculated value based on the sum. Qualifying ordisqualifying the calculated value may include, for example, comparingthe sum to a threshold. In some embodiments, the processing equipmentmay determine an absolute value of the difference between each samplepoint and corresponding sample point. In some embodiments, thecalculated value may be indicative of a period associated with thephysiological rate, and the particular spacing between correspondingsample points may be substantially equal to the period.

In some embodiments, the processing equipment may generate value pairseach including a first sample point of the physiological signal and asecond point of the physiological signal spaced apart by a particularspacing based on a calculated value. The processing equipment maydetermine a best fit linear relationship based on the plurality of valuepairs, determine at least one statistical metric based on the linearrelationship and the value pairs, and qualify or disqualify thecalculated value based on the at least one statistical metric. In someembodiments, the statistical metric may include a standard error betweenthe value pairs and the linear relationship, as well as a slope of thelinear relationship. In some embodiments, qualifying or disqualifyingthe calculated value may include determining a value indicative of theprobability, relative to a predetermined probability distributionfunction, of the at least one statistical metric being outside of a setof bounding values. In some embodiments, the processing equipment maydetermine at least one additional spacing other than the particularspacing. For each of the at least one additional spacing, the processingequipment may generate value pairs each including a first sample pointand a second point of the physiological signal spaced apart by therespective spacing, determine a best fit linear relationship based onthe respective plurality of value pairs, and determine at least onestatistical metric based on the respective linear relationship and therespective plurality of value pairs. The processing equipment maydetermine a value indicative of the probability, relative to apredetermined probability distribution function, of the respective atleast one statistical metric being outside of the set of boundingvalues, and calculate a sum based on the values indicative of theprobability of the respective at least one statistical metric beingoutside of the set of bounding values for the at least one additionallag value and the value indicative of the probability of the at leastone statistical metric being outside of the set of bounding values forthe particular spacing. Qualifying or disqualifying the calculated valuemay be, for example, based on the sum. In some embodiments, determiningthe probability of the statistical metric being outside of the set ofbounding values may include using a reference look-up table comprising aset of probability values.

In some embodiments, the processing equipment may generate four sorteddifference signals based on four respective segments of thephysiological signal. The processing equipment may analyze first andsecond sorted difference signals to determine at least one first metric,analyze third and fourth sorted difference signals to determine at leastone second metric, and qualify or disqualify a calculated value based onthe at least one first metric and the at least one second metric. Acalculated value, for example, indicative of a period associated with aphysiological rate may be received. In some embodiments, the thirdsegment may be a subset of the first segment corresponding to the periodassociated with the physiological rate, and the fourth segment may be asubset of the second segment corresponding to the period associated withthe physiological rate. The at least one first metric may include, forexample, a correlation coefficient between the first segment and thesecond segment based on a set of value pairs including a value of thefirst sorted difference signal and a corresponding value of the secondsorted difference signal, a value indicative of slope based on a set ofvalue pairs including a value of the first sorted difference signal anda corresponding value of the second sorted difference signal, a valueindicative of a curve length of the first sorted difference signal, avalue indicative of a curve length of the second sorted differencesignal, a value of the first sorted difference signal corresponding to aterminal end of the first sorted difference signal, a value of thesecond sorted difference signal corresponding to a terminal end of thesecond sorted difference signal, and a combination thereof. In someembodiments, the at least one first metric may include at least twometrics, and the processing equipment may qualify or disqualify thecalculated value by comparing a first metric of the at least two metricsto a second metric of the at least two metrics. In some embodiments,qualifying or disqualifying the calculated value may include comparingthe at least one first metric to a threshold. In some embodiments,qualifying or disqualifying the calculated value may include comparingthe at least one first metric to the at least one second metric.

In some embodiments, the processing equipment may determine acorrelation lag value corresponding to a peak in a correlation sequence,determine a correlation value at a second lag value equal to a fractionof the correlation lag value, and qualify or disqualify the correlationlag value based on the correlation value at the second lag value. Thesecond lag value may be, for example, equal to substantially one half ofthe correlation lag value. In some embodiments, qualifying ordisqualifying the correlation lag value may include comparing thecorrelation value at the second lag value to a threshold. In someembodiments, qualifying or disqualifying the correlation lag value mayinclude comparing the correlation value at the second lag value to thecorrelation sequence value at the correlation lag value.

In some embodiments, the processing equipment may determine a firstvalue indicative of a baseline of the physiological signal, determine asecond value indicative of a deviation of the physiological signal fromthe baseline, and qualify or disqualify a calculated value based on thefirst value and the second value. The first value may be, for example,selected from a median value of the physiological signal, an average ofthe physiological signal, a coefficient corresponding to a best fitcurve of the physiological signal, and a combination thereof. The secondvalue may be, for example, selected from a standard deviation valuebased on the physiological signal, a standard error between thephysiological signal and the first value, a root mean square value basedon the physiological signal, and a combination thereof. In someembodiments, the processing equipment may perform signal conditioning onthe physiological signal based on the calculated value to generate aconditioned signal, and the second value may be indicative of adeviation of the conditioned signal from the baseline. In someembodiments, qualifying or disqualifying the calculated value may bebased on a ratio of the second value to the first value. For example,the ratio may be multiplied by a coefficient and compared to a thresholdto determine whether to qualify or disqualify the calculated value. Insome embodiments, the processing equipment may qualify or disqualify thecalculated value based on comparing a metric derived from the first andsecond values to a history of metric values calculated at respectiveprevious times.

In some embodiments, the processing equipment may generate a firstsorted difference signal based on a first segment of the physiologicalsignal having a size corresponding to a period associated with apotential physiological rate of a subject, generate a second sorteddifference signal based on a second segment of the physiological signalhaving a size corresponding to a fraction of the period, and generate athird difference signal based on a third segment of the physiologicalsignal having a size corresponding to a multiple of the period. Theprocessing equipment may analyze the first, second, and third sorteddifference signals, and qualify or disqualify a calculated value basedon the analysis. The period may be, for example, derived from thecalculated value. In some embodiments, qualifying or disqualifying thecalculated value may include comparing at least two of the first,second, third sorted difference signals to each other. In someembodiments, the processing equipment may compare at least one of thefirst, second, and third sorted difference signals to a referencedistribution. For example the processing equipment may use a look-uptable to determine one or more reference values, and qualify ordisqualify the calculated value based on the one or more referencevalues. In some embodiments, the processing equipment may identify thesecond segment by determining a standard deviation value for each of aplurality of different segments within the physiological signal, havingrespective sizes corresponding to the fraction of the period, andidentifying the segment having the maximum standard deviation value ofthe plurality of standard deviation values. In some embodiments,qualifying or disqualifying the calculated value may include determiningwhether the calculated value is likely a harmonic of an actual periodassociated with a physiological rate of the subject based on analyzingthe first, second, and third sorted difference signals.

In some embodiments, the processing equipment may determine a valueindicative of noise in the physiological signal, adjust at least onecriterion for qualifying or disqualifying a calculated value based onthe value indicative of noise, and qualify or disqualify the calculatedvalue based on the at least one adjusted criterion. Adjusting the atleast one criterion may include, for example, loosening the criterionwhen the value indicative of noise exceeds a threshold. In a furtherexample, adjusting the at least one criterion may include tightening thecriterion when the value indicative of noise is below a threshold. In afurther example, adjusting the at least one criterion may includeadjusting a type of the criterion. In some embodiments, the at least onecriterion may include a threshold, and the processing equipment mayqualify or disqualify the calculated value by determining a metric basedon the physiological signal, and comparing the metric to the threshold.

In some embodiments, the processing equipment may filter a physiologicalsignal based on an adjustable filter to generate a filteredphysiological signal, and perform calculations over time based on thefiltered physiological signal to determine values indicative of aphysiological parameter. The adjustable filter may be, for example,adjusted based on the values indicative of the physiological parameter.Some of the calculations performed over time are qualified, while someare disqualified. The processing equipment may determine a metric basedon the physiological signal, where the metric is used to determinewhether to output a value based on one or more previously calculatedvalues when a current calculation is disqualified. The processingequipment may output a value based on one or more previously calculatedvalues when a current calculation is disqualified and a criterion basedon the metric is satisfied. Performing the calculations over time mayinclude, for example, determining a sequence of correlation lag values.The processing equipment may maintain a counter that adjusts a countervalue based on whether calculations are qualified or disqualified andthe criterion may be further based on the counter value. In someembodiments, the metric may be a noise metric based on the physiologicalsignal, and the processing equipment may maintain a counter thataugments a counter value when a calculation is disqualified, determine athreshold based on the noise metric, and output the value based on oneor more previously calculated values value of the physiologicalparameter based on a comparison of the counter value to the threshold.In some embodiments, the threshold increases when the noise metricincreases. In some embodiments, the filter may include a bandpass filterhaving at least one adjustable setting, and the processing equipment mayadjust the at least one setting based on the metric.

BRIEF DESCRIPTION OF THE FIGURES

The above and other features of the present disclosure, its nature andvarious advantages will be more apparent upon consideration of thefollowing detailed description, taken in conjunction with theaccompanying drawings in which:

FIG. 1 shows an illustrative physiological monitoring system, inaccordance with some embodiments of the present disclosure;

FIG. 2 is a block diagram of the illustrative physiological monitoringsystem of FIG. 1, which may be coupled to a subject, in accordance withsome embodiments of the present disclosure;

FIG. 3 is an illustrative signal processing system in accordance withsome embodiments of the present disclosure;

FIG. 4 is a flow diagram of illustrative steps for determiningphysiological information of a subject, in accordance with someembodiments of the present disclosure;

FIG. 5 is a flow diagram of illustrative steps for initializing atechnique for determining physiological information, in accordance withsome embodiments of the present disclosure;

FIG. 6 is a table of illustrative status flags, in accordance with someembodiments of the present disclosure;

FIG. 7 is a block diagram of illustrative memory including ratealgorithm information, in accordance with some embodiments of thepresent disclosure.

FIG. 8 is a flow diagram of illustrative steps for managing a statusindicator, in accordance with some embodiments of the presentdisclosure;

FIG. 9 is a block diagram of illustrative techniques for managingalgorithm settings, in accordance with some embodiments of the presentdisclosure;

FIG. 10 is a flow diagram of illustrative steps for managing algorithmsettings using a classification, in accordance with some embodiments ofthe present disclosure;

FIG. 11 is a flow diagram of illustrative steps for classifyingphysiological data, in accordance with some embodiments of the presentdisclosure;

FIG. 12 is a panel showing two plots of illustrative physiologicalsignals, one of which exhibits a dicrotic notch, in accordance with someembodiments of the present disclosure;

FIG. 13 is a flow diagram of illustrative steps for determining analgorithm setting based on an offset of positive and negative values ofa difference signal, in accordance with some embodiments of the presentdisclosure;

FIG. 14 is a panel showing two illustrative difference signals derivedfrom physiological signals, one of which exhibits a dicrotic notch,along with sorted positive and negative values, in accordance with someembodiments of the present disclosure;

FIG. 15 is a flow diagram of illustrative steps for determining analgorithm setting based on a sorted difference signal, in accordancewith some embodiments of the present disclosure;

FIG. 16 is a panel showing a sorted difference signal and twohistograms, in accordance with some embodiments of the presentdisclosure;

FIG. 17 is a flow diagram of illustrative steps for determining analgorithm setting based on area ratios of positive and negative regionsof a difference signal, in accordance with some embodiments of thepresent disclosure;

FIG. 18 is a panel showing two illustrative plots of respectivedifference signals having positive and negative regions, in accordancewith some embodiments of the present disclosure;

FIG. 19 is a flow diagram of illustrative steps for determining analgorithm setting based on first and second difference signals, inaccordance with some embodiments of the present disclosure;

FIG. 20 is a panel showing illustrative PPG signals with and without adicrotic notch, and corresponding first and second difference signalsfor each, in accordance with some embodiments of the present disclosure;

FIG. 21 is a flow diagram of illustrative steps for determining analgorithm setting based on a skewness value of a physiological signal,in accordance with some embodiments of the present disclosure;

FIG. 22 is a panel showing an illustrative contour plot of instances ofskewness value and correlation lag value, in accordance with someembodiments of the present disclosure;

FIG. 23 is a flow diagram of illustrative steps for determining analgorithm setting based on a skewness value and a difference signal ofthe physiological signal, in accordance with some embodiments of thepresent disclosure;

FIG. 24 is a panel showing an illustrative plot of classifier data basedon a skewness value and a sorted difference signal, in accordance withsome embodiments of the present disclosure;

FIG. 25 is a flow diagram of illustrative steps for determining analgorithm setting based on a combination of metrics, in accordance withsome embodiments of the present disclosure;

FIG. 26 is a panel showing an illustrative set of de-trending metricvalues and illustrative contours for an illustrative combination ofde-trending metrics, in accordance with some embodiments of the presentdisclosure;

FIG. 27 is a panel showing three illustrative sets of de-trending metricvalues, taken for different window sizes, in accordance with someembodiments of the present disclosure;

FIG. 28 is a flow diagram of illustrative steps for temporallymonitoring metrics, in accordance with some embodiments of the presentdisclosure;

FIG. 29 is a panel showing illustrative PPG signals with and without adicrotic notch, corresponding difference signals for each, andcorresponding sorted difference signals for each in accordance with someembodiments of the present disclosure;

FIG. 30 is a flow diagram of illustrative steps for determining a noisemetric from a line fit of a sorted difference signal, in accordance withsome embodiments of the present disclosure;

FIG. 31 is a panel showing illustrative PPG signals with and without adicrotic notch, corresponding difference signals for each, correspondingsorted difference signals for each, and corresponding line fits foreach, in accordance with some embodiments of the present disclosure;

FIG. 32 is a flow diagram of illustrative steps for determining a noisemetric from a segmented line fit of a sorted difference signal, inaccordance with some embodiments of the present disclosure;

FIG. 33 is a panel showing an illustrative difference signal derivedfrom a PPG signal, a sorted difference signal, and correspondingsegmented line fits, in accordance with some embodiments of the presentdisclosure;

FIG. 34 is a partial view of the sorted difference signal of FIG. 33,taken from circle 3400, showing portions of two groups, in accordancewith some embodiments of the present disclosure;

FIG. 35 is a plot of an illustrative first segment of a sorteddifference signal, and corresponding thresholds, in accordance with someembodiments of the present disclosure;

FIG. 36 is a flow diagram of illustrative steps for determining a noisemetric based on identified noise points, in accordance with someembodiments of the present disclosure;

FIG. 37 is a flow diagram of illustrative steps for determining a noisemetric based on two portions of physiological data, in accordance withsome embodiments of the present disclosure;

FIG. 38 is a panel showing an illustrative PPG signal, differencesignals derived from the PPG signal, and corresponding sorted differencesignals, in accordance with some embodiments of the present disclosure;

FIG. 39 is a flow diagram of illustrative steps for estimatingsignal-to-noise ratio based on a sorted difference signal, in accordancewith some embodiments of the present disclosure;

FIG. 40 is a panel showing an illustrative contour plot of instances ofsignal-to-noise ratio values and ordered statistic noise metric values,in accordance with some embodiments of the present disclosure;

FIG. 41 is a flow diagram of illustrative steps for determining aresultant noise metric based on a combination of noise metrics, inaccordance with some embodiments of the present disclosure;

FIG. 42 is a flow diagram of illustrative steps for modifyingphysiological data using an envelope, in accordance with someembodiments of the present disclosure;

FIG. 43 is a flow diagram of illustrative steps for modifyingphysiological data by subtracting a trend, in accordance with someembodiments of the present disclosure;

FIG. 44 is a plot of an illustrative window of data with the meanremoved, in accordance with some embodiments of the present disclosure;

FIG. 45 is a plot of an illustrative window of data and a quadratic fit,in accordance with some embodiments of the present disclosure;

FIG. 46 is a plot of the illustrative window of data of FIG. 45 with thequadratic fit subtracted, in accordance with some embodiments of thepresent disclosure;

FIG. 47 is a plot of an illustrative modified window of data derivedfrom an original window of data with the mean subtracted, in accordancewith some embodiments of the present disclosure;

FIG. 48 is a plot of an illustrative modified window of data derivedfrom the same original window of data as FIG. 47 with a linear baselinesubtracted, in accordance with some embodiments of the presentdisclosure;

FIG. 49 is a plot of an illustrative modified window of data derivedfrom the same original windows of data as FIGS. 47 and 48 with aquadratic baseline subtracted, in accordance with some embodiments ofthe present disclosure;

FIG. 50 is a flow diagram of illustrative steps for modifyingphysiological data using a derivative limiter, in accordance with someembodiments of the present disclosure;

FIG. 51 is a panel of three plots showing an illustrative window of datahaving a baseline shift, a first derivative of the window of data, and amodified window of data, in accordance with some embodiments of thepresent disclosure;

FIG. 52 is a flow diagram of illustrative steps for modifyingphysiological data using a stability function, in accordance with someembodiments of the present disclosure;

FIG. 53 is a panel of three plots showing an illustrative window ofdata, a stability function, and a derivative of the stability function,in accordance with some embodiments of the present disclosure;

FIG. 54 is a flow diagram of illustrative steps for modifyingphysiological data using a corrected difference signal, in accordancewith some embodiments of the present disclosure;

FIG. 55 is a panel of plots showing an illustrative difference signal, asorted difference signal, a corrected difference signal, and a correcteddifference signal, in accordance with some embodiments of the presentdisclosure;

FIG. 56 is a flow diagram of illustrative steps for modifyingphysiological data using a positive signal and a negative signal, inaccordance with some embodiments of the present disclosure;

FIG. 57 is a panel of five plots showing an illustrative window ofphysiological data having varying amplitude, filtered signals, combinedsignals, and a modified window of data, in accordance with someembodiments of the present disclosure;

FIG. 58 is a flow diagram of illustrative steps for modifyingphysiological data using a filtered signal, in accordance with someembodiments of the present disclosure;

FIG. 59 is a panel of six plots showing an illustrative window ofphysiological data, an absolute value signal, a filtered signal, ashifted signal, and a modified window of data, in accordance with someembodiments of the present disclosure;

FIG. 60 is a flow diagram of illustrative steps for selectively applyinga filter to physiological data, in accordance with some embodiments ofthe present disclosure;

FIG. 61 is a flow diagram of illustrative steps for applying a bandpassfilter having adjustable settings to physiological data, in accordancewith some embodiments of the present disclosure;

FIG. 62 is a flow diagram of illustrative steps for performing acorrelation using a window of physiological data, in accordance withsome embodiments of the present disclosure;

FIG. 63 is a flow diagram of illustrative steps for generating acorrelation sequence using a window of physiological data, in accordancewith some embodiments of the present disclosure;

FIG. 64 is a diagram showing an illustrative window of physiologicaldata and a template at several lags, in accordance with some embodimentsof the present disclosure;

FIG. 65 is a plot showing an illustrative correlation sequence for awindow of physiological data, in accordance with some embodiments of thepresent disclosure;

FIG. 66 is a flow diagram of illustrative steps for identifying a peakof a correlation output greater than a threshold, in accordance withsome embodiments of the present disclosure;

FIG. 67 is a flow diagram of further illustrative steps for identifyinga peak of a correlation output greater than a threshold, in accordancewith some embodiments of the present disclosure;

FIG. 68 is a flow diagram of illustrative steps for identifying a peakof a correlation output as the correlation output is generated, inaccordance with some embodiments of the present disclosure;

FIG. 69 is a plot showing an illustrative correlation sequence for awindow of physiological data, and several thresholds, in accordance withsome embodiments of the present disclosure;

FIG. 70 is a flow diagram of illustrative steps for performing acorrelation calculation using a correlation matrix, in accordance withsome embodiments of the present disclosure;

FIG. 71 is a block diagram showing an illustrative window ofphysiological data with generalized templates and lags, in accordancewith some embodiments of the present disclosure;

FIG. 72 is a diagram showing an illustrative lag matrix and correlationmatrix, in accordance with some embodiments of the present disclosure;

FIG. 73 is a plot showing a graphical representation of an illustrativecorrelation matrix, in accordance with some embodiments of the presentdisclosure;

FIG. 74 is a plot showing a graphical representation of the illustrativecorrelation matrix of FIG. 73 facing a primary direction, in accordancewith some embodiments of the present disclosure;

FIG. 75 is a panel of three plots showing an illustrative window ofdata, and two sets of two segments of physiological data having arelative lag, in accordance with some embodiments of the presentdisclosure;

FIG. 76 is a panel of two illustrative plots each showing a set of atemplate and corresponding data of FIG. 75 plotted against each other,in accordance with some embodiments of the present disclosure;

FIG. 77 is a flow diagram of illustrative steps for determining a metricfrom two segments of physiological data, and using the metric to modifycorrelation output or identify a correlation peak, in accordance withsome embodiments of the present disclosure;

FIG. 78 is a panel of two plots corresponding to the plots of FIG. 76after an illustrative transformation of the data, in accordance withsome embodiments of the present disclosure;

FIG. 79 is a flow diagram of illustrative steps for determining a metricfrom a vertical distribution, and using the metric to modify correlationoutput or identify a correlation peak, in accordance with someembodiments of the present disclosure;

FIG. 80 is a panel of the two plots of FIG. 78 and two respective plotsof corresponding vertical distributions, in accordance with someembodiments of the present disclosure;

FIG. 81 is a flow diagram of illustrative steps analyzing a verticaldistribution, in accordance with some embodiments of the presentdisclosure;

FIG. 82 is a flow diagram of illustrative steps for determining a metricfrom a horizontal distribution, and using the metric to modifycorrelation output or identify a correlation peak, in accordance withsome embodiments of the present disclosure;

FIG. 83 is a panel of the two plots of FIG. 76 and two respective plotsof corresponding horizontal distributions, in accordance with someembodiments of the present disclosure;

FIG. 84 is a panel of the two plots of illustrative cumulativedistributions of horizontal values, in accordance with some embodimentsof the present disclosure;

FIG. 85 is a flow diagram of illustrative steps for applying statisticalregression analysis, in accordance with some embodiments of the presentdisclosure;

FIG. 86 is a flow diagram of illustrative steps for qualifying ordisqualifying a value that may be indicative of a physiological rateusing a cross-correlation, in accordance with some embodiments of thepresent disclosure;

FIG. 87 is a plot of an illustrative PPG signal showing one pulse of asubject, in accordance with some embodiments of the present disclosure;

FIG. 88 is a plot of an illustrative template derived from the PPGsignal of FIG. 87 with baseline removed, in accordance with someembodiments of the present disclosure;

FIG. 89 is a plot of the illustrative template of FIG. 88 scaled todifferent sizes for use as templates in performing a cross-correlation,in accordance with some embodiments of the present disclosure;

FIG. 90 is a plot of output of an illustrative cross-correlation betweena photoplethysmograph signal or a signal derived thereof and apredefined template, in accordance with some embodiments of the presentdisclosure;

FIG. 91 is a flow diagram of illustrative steps for qualifying ordisqualifying a value that may be indicative of a physiological ratebased on an analysis of two segments of a cross-correlation output, inaccordance with some embodiments of the present disclosure;

FIG. 92 is a plot of an illustrative cross-correlation output, showingseveral reference points for selecting two segments and generating asymmetry curve, in accordance with some embodiments of the presentdisclosure;

FIG. 93 is a plot of an illustrative symmetry curve generated using twosegments of a cross-correlation, in accordance with some embodiments ofthe present disclosure;

FIG. 94 is a flow diagram of illustrative steps for qualifying ordisqualifying one or more values that may be indicative of aphysiological rate based on an analysis of offset segments of across-correlation signal, in accordance with some embodiments of thepresent disclosure;

FIG. 95 shows four sets of plots of illustrative cross-correlationsignals and corresponding symmetry curves, radial curves, radiuscalculations, and angle calculations, in accordance with someembodiments of the present disclosure;

FIG. 96 is a flow diagram of illustrative steps for qualifying ordisqualifying a value that may be indicative of a physiological ratebased on a state transition, in accordance with some embodiments of thepresent disclosure;

FIG. 97 is a flow diagram of illustrative steps for qualifying ordisqualifying a value that may be indicative of a physiological ratebased on a skew value, in accordance with some embodiments of thepresent disclosure;

FIG. 98 is a flow diagram of illustrative steps for qualifying ordisqualifying one or more values that may be indicative of aphysiological rate based on an areas of positive and negative portionsof segments of a cross-correlation output, in accordance with someembodiments of the present disclosure;

FIG. 99 is a plot of an illustrative cross-correlation output, centeredabout zero with a correctly determined correlation lag value, inaccordance with some embodiments of the present disclosure;

FIG. 100 is a plot of an illustrative cross-correlation output with acorrelation lag value incorrectly determined to be double the correctrate, in accordance with some embodiments of the present disclosure;

FIG. 101 is a plot of an illustrative cross-correlation output with thecorrelation lag value incorrectly determined to be half the correctrate, in accordance with some embodiments of the present disclosure;

FIG. 102 is a plot of illustrative difference calculations of the AreaTest, and a plot of illustrative calculated rates indicative of anactual physiological heart rate, in accordance with some embodiments ofthe present disclosure;

FIG. 103 is a flow diagram of illustrative steps for qualifying ordisqualifying one or more values that may be indicative of aphysiological rate based on a filtered physiological signal, inaccordance with some embodiments of the present disclosure;

FIG. 104 is a flow diagram of illustrative steps for qualifying ordisqualifying one or more values that may be indicative of aphysiological rate based on a comparison of areas of two segments of across-correlation signal, in accordance with some embodiments of thepresent disclosure;

FIG. 105 is a flow diagram of illustrative steps for qualifying ordisqualifying a value that may be indicative of a physiological ratebased on statistical properties of a cross-correlation output, inaccordance with some embodiments of the present disclosure;

FIG. 106 is a flow diagram of illustrative steps for qualifying ordisqualifying a value that may be indicative of a physiological ratebased on differences of a physiological signal, in accordance with someembodiments of the present disclosure;

FIG. 107 is a flow diagram of illustrative steps for qualifying ordisqualifying a value that may be indicative of a physiological ratebased on a half lag analysis, in accordance with some embodiments of thepresent disclosure;

FIG. 108 is a flow diagram of illustrative steps for qualifying ordisqualifying a value that may be indicative of a physiological ratebased on a sorted difference signal, in accordance with some embodimentsof the present disclosure;

FIG. 109 is a block diagram of illustrative physiological data and fouridentified segments, in accordance with some embodiments of the presentdisclosure;

FIG. 110 is a panel of illustrative plots showing paired sorteddifference signals, in accordance with some embodiments of the presentdisclosure;

FIG. 111 is a flow diagram of illustrative steps for qualifying ordisqualifying a value that may be indicative of a physiological ratebased on analyzing harmonic sorted difference signals, in accordancewith some embodiments of the present disclosure;

FIG. 112 is a panel of illustrative plots showing sorted differencesignals for a lag, half lag, and double lag segment of physiologicaldata, in accordance with some embodiments of the present disclosure;

FIG. 113 is a panel of illustrative plots showing physiological data andthree selected segments, in accordance with some embodiments of thepresent disclosure;

FIG. 114 is an illustrative plot showing a contour plot representationof a look-up table for qualifying or disqualifying a correlation lagvalue based on analyzing harmonic sorted difference signals, inaccordance with some embodiments of the present disclosure;

FIG. 115 is a flow diagram of illustrative steps for qualifying ordisqualifying a value that may be indicative of a physiological ratebased on a standard deviation ratio (SDR) metric, in accordance withsome embodiments of the present disclosure;

FIG. 116 is a flow diagram of illustrative steps for implementing astandard deviation ratio (SDR) technique, in accordance with someembodiments of the present disclosure;

FIG. 117 is a panel of illustrative plots showing a physiologicalsignal, an SDR signal, an SDR threshold, and a test outcome signal, inaccordance with some embodiments of the present disclosure;

FIG. 118 is a flow diagram of illustrative steps for qualifying ordisqualifying a value that may be indicative of a physiological ratebased on a standard deviation ratio (SDR) metric, in accordance withsome embodiments of the present disclosure;

FIG. 119 is a flow diagram of illustrative steps for qualifying ordisqualifying a value that may be indicative of a physiological ratebased on a p-value, in accordance with some embodiments of the presentdisclosure;

FIG. 120 is a flow diagram of illustrative steps for qualifying ordisqualifying a value that may be indicative of a physiological ratebased on a maximum and minimum of a correlation sequence, in accordancewith some embodiments of the present disclosure;

FIG. 121 is a flow diagram of illustrative steps for adjustingqualification or disqualification criteria based on noise, in accordancewith some embodiments of the present disclosure;

FIG. 122 is a flow diagram of illustrative steps for adjusting aqualification or disqualification criterion based on a value indicativeof a physiological rate, in accordance with some embodiments of thepresent disclosure;

FIG. 123A is a flow diagram of illustrative steps for combiningqualification tests, in accordance with some embodiments of the presentdisclosure;

FIG. 123B is a block diagram of an illustrative neural network that mayreceive a combination of inputs, in accordance with some embodiments ofthe present disclosure;

FIG. 124 is a flow diagram of illustrative steps for combiningqualification tests, in accordance with some embodiments of the presentdisclosure;

FIG. 125 is a flow diagram of illustrative steps for analyzingqualification metrics based on scaled templates of different lengths, inaccordance with some embodiments of the present disclosure;

FIG. 126 is a flow diagram of illustrative steps for selecting one ormore templates, and analyzing qualification metrics based on scaledtemplates, in accordance with some embodiments of the presentdisclosure;

FIG. 127 is a flow diagram of illustrative steps for managing posting avalue indicative of a physiological parameter, in accordance with someembodiments of the present disclosure;

FIG. 128 is a flow diagram of illustrative modes of a rate algorithm, inaccordance with some embodiments of the present disclosure;

FIG. 129 is a flow diagram of illustrative steps for calculating andposting a physiological rate, in accordance with some embodiments of thepresent disclosure;

FIG. 130 is a flow diagram of illustrative steps for determiningde-trending settings and qualification settings, in accordance with someembodiments of the present disclosure;

FIG. 131 is a flow diagram of illustrative steps for pre-processingphysiological data, in accordance with some embodiments of the presentdisclosure;

FIG. 132 is a flow diagram of illustrative steps for furtherpre-processing physiological data, in accordance with some embodimentsof the present disclosure;

FIG. 133 is a flow diagram of illustrative steps for qualifying ordisqualifying a correlation lag value, in accordance with someembodiments of the present disclosure;

FIG. 134 is a flow diagram of illustrative steps for managing algorithmsettings when a correlation lag value is disqualified, in accordancewith some embodiments of the present disclosure;

FIG. 135 is a flow diagram of illustrative steps for managing algorithmsettings when a correlation lag value is qualified, in accordance withsome embodiments of the present disclosure; and

FIG. 136 is a flow diagram of illustrative steps for determining aphysiological parameter using more than one algorithm mode in parallel,in accordance with some embodiments of the present disclosure.

DETAILED DESCRIPTION OF THE FIGURES

The present disclosure is directed towards determining physiologicalinformation including physiological rate information. A physiologicalmonitor may determine one or more physiological parameters such as, forexample, pulse rate, respiration rate, blood oxygen saturation, bloodpressure, or any other suitable parameters, based on one or more signalsreceived from one or more sensors. For example, a physiological monitormay analyze a photoplethysmographic (PPG) signal for oscillometricbehavior associated with a pulse rate, a respiration rate, or both.Physiological signals may include desired and undesired signalcomponents. For example, physiological signals may include one or morenoise components, which may include the effects of ambient light,electromagnetic radiation from powered devices (e.g., at 50 Hz or 60Hz), subject movement, any other non-physiological signal component orundesired physiological signal component, or any combination thereof.

In some circumstances, the determination of a pulse rate from PPGinformation may present challenges. Typical pulse rates range from about20 to 300 BM for human subjects. For example, the pulse rate of aneonate may be relatively high (e.g., 130-180 BPM) compared to that of aresting adult (e.g., 50-80 BPM). Various sources of noise may obscurethe pulse rate. For example, motion artifacts from subject movement mayoccur over time scales similar to those of the pulse rate of the subject(e.g., on the order of 1 Hertz). Subject movement can prove especiallytroublesome in measuring pulse rates of neonates, who tend to exhibitsignificant movement at times during measurements. Significant movementcan cause the noise component of the PPG signal to be larger and in somecases significantly larger than the desired physiological pulsecomponent.

Other factors may also present challenges in determining pulse rate. Forexample, the shape of physiological pulses can vary significantly notonly between subjects, but also over time for subjects. Moreover,certain pulse shapes in particular may make it difficult to determinethe correct pulse rate. As an example, deep dicrotic notches may cause asingle pulse to appear similar to two consecutive pulses and thus it maycause the determined pulse rate to be double the actual rate. Lowperfusion is another factor that can present challenges. A subject withlow perfusion typically has low-amplitude physiological pulses andtherefore PPG signals derived from such subjects may be more susceptibleto noise than PPG signals derived from other subjects.

A physiological signal, a signal derived thereof, or a metric derivedthereof may be analyzed to determine whether the physiological signal isindicative of a desired physiological activity. Varying levels of rigor,types of qualifications, types of de-trending, or other processingcharacteristics may be used during the analysis, based oncharacteristics of the signal or one or more derived metrics. Forexample, signals exhibiting relatively large amounts of noise may beanalyzed differently than signals exhibiting relatively less noise. In afurther example, PPG signals exhibiting a dicrotic notch may be analyzeddifferently than PPG signals exhibiting no dicrotic notch.

The present disclosure discloses techniques for reliably determiningrate information from a physiological signal and in particular todetermining pulse rate from photoplethysmographic information. Thepresent disclosure also discloses techniques for determining noiseinformation from a physiological signal. The present disclosure alsodiscloses techniques for conditioning physiological signals. The presentdisclosure also discloses techniques for qualifying physiologicalinformation. While the disclosed techniques are described in someembodiments as being implemented in the context of oximeters, it will beunderstood that any suitable processing device may be used in accordancewith the present disclosure.

An oximeter is a medical device that may determine the oxygen saturationof the blood. One common type of oximeter is a pulse oximeter, which mayindirectly measure the oxygen saturation of a subject's blood (asopposed to measuring oxygen saturation directly by analyzing a bloodsample taken from the subject). Pulse oximeters may be included inphysiological monitoring systems that measure and display various bloodflow characteristics including, but not limited to, the oxygensaturation of hemoglobin in arterial blood. Such physiologicalmonitoring systems may also measure and display additional physiologicalparameters, such as a subject's pulse rate, respiration rate, and bloodpressure.

An oximeter may include a light sensor that is placed at a site on asubject, typically a fingertip, toe, forehead or earlobe, or in the caseof a neonate, across a foot. The oximeter may use a light source to passlight through blood perfused tissue and photoelectrically sense thetransmission of the light in the tissue. In addition, locations whichare not typically understood to be optimal for pulse oximetry may beused in some embodiments. For example, additional suitable sensorlocations include, without limitation, the neck to monitor carotidartery pulsatile flow, the wrist to monitor radial artery pulsatileflow, the inside of a subject's thigh to monitor femoral arterypulsatile flow, the ankle to monitor tibial artery pulsatile flow, andaround or in front of the ear. Suitable sensors for these locations mayinclude sensors for sensing attenuated light based on detectingreflected light. In all suitable locations, for example, the oximetermay measure the intensity of light that is received at the light sensoras a function of time. The oximeter may also include sensors at multiplelocations. A signal representing light intensity versus time or amathematical manipulation of this signal (e.g., a scaled versionthereof, a log taken thereof, a scaled version of a log taken thereof,etc.) may be referred to as a photoplethysmograph (PPG) signal. Inaddition, the term “PPG signal,” as used herein, may also refer to anabsorption signal (i.e., representing the amount of light absorbed bythe tissue) or any suitable mathematical manipulation thereof. The lightintensity or the amount of light absorbed may then be used to calculateany of a number of physiological parameters, including an amount of ablood constituent (e.g., oxyhemoglobin) being measured as well as apulse rate and when each individual pulse occurs.

In some applications, the light passed through the tissue is selected tobe of one or more wavelengths that are absorbed by the blood in anamount representative of the amount of the blood constituent present inthe blood. The amount of light passed through the tissue varies inaccordance with the changing amount of blood constituent in the tissueand the related light absorption. Red and infrared (IR) wavelengths maybe used because it has been observed that highly oxygenated blood willabsorb relatively less Red light and more IR light than blood with alower oxygen saturation. By comparing the intensities of two wavelengthsat different points in the pulse cycle, it is possible to estimate theblood oxygen saturation of hemoglobin in arterial blood.

When the measured blood parameter is the oxygen saturation ofhemoglobin, a convenient starting point assumes a saturation calculationbased on Lambert-Beer's law. The following notation will be used herein:I(λ,t)=I ₀(λ)exp(−(sβ ₀(λ)+(1−s)β_(r)(λ))l(t)).  (1)where:λ=wavelength;t=time;I=intensity of light detected;I₀=intensity of light transmitted;s=oxygen saturation;β₀, β_(r)=absorption coefficients (e.g., empirically derived); andl(t)=a combination of concentration and path length from emitter todetector as a function of time.

In some embodiments, a physiological system may measure light absorptionat two wavelengths (e.g., Red and IR), and then calculates saturation bysolving for the “ratio of ratios” as follows.

1. The natural logarithm of Eq. 1 is taken (“log” will be used torepresent the natural logarithm) for IR and Red to yield:log I=log I ₀−(sβ ₀+(1−s)β_(r))l(t)).  (2)2. Eq. 2 is then differentiated with respect to time to yield thefollowing:

$\begin{matrix}{\frac{{\mathbb{d}\log}\mspace{11mu} I}{\mathbb{d}t} = {{- \left( {{s\;\beta_{0}} + {\left( {1 - s} \right)\beta_{r}}} \right)}{\frac{\mathbb{d}l}{\mathbb{d}t}.}}} & (3)\end{matrix}$3. Eq. 3, evaluated at the Red wavelength λ_(R), is divided by Eq. 3evaluated at the IR wavelength λ_(IR) in accordance with the following:

$\begin{matrix}{\frac{{\mathbb{d}\log}\mspace{11mu}{{I\left( \lambda_{R} \right)}/{\mathbb{d}t}}}{{\mathbb{d}\log}\mspace{11mu}{{I\left( \lambda_{IR} \right)}/{\mathbb{d}t}}} = {\frac{- \left( {{s\;{\beta_{0}\left( \lambda_{R} \right)}} + {\left( {1 - s} \right){\beta_{r}\left( \lambda_{R} \right)}}} \right)}{- \left( {{s\;{\beta_{0}\left( \lambda_{IR} \right)}} + {\left( {1 - s} \right){\beta_{r}\left( \lambda_{IR} \right)}}} \right)}.}} & (4)\end{matrix}$4. Solving for s yields the following:

$\begin{matrix}{s = {\frac{{\frac{{\mathbb{d}\log}\mspace{11mu}{I\left( \lambda_{IR} \right)}}{\mathbb{d}t}{\beta_{r}\left( \lambda_{R} \right)}} - {\frac{{\mathbb{d}\log}\mspace{11mu}{I\left( \lambda_{R} \right)}}{\mathbb{d}t}{\beta_{r}\left( \lambda_{IR} \right)}}}{\begin{matrix}{{\frac{{\mathbb{d}\log}\mspace{11mu}{I\left( \lambda_{R} \right)}}{\mathbb{d}t}\left( {{\beta_{0}\left( \lambda_{IR} \right)} - {\beta_{r}\left( \lambda_{IR} \right)}} \right)} -} \\{\frac{{\mathbb{d}\log}\mspace{11mu} I\;\left( \lambda_{IR} \right)}{\mathbb{d}t}\left( {{\beta_{0}\left( \lambda_{R} \right)} - {\beta_{r}\left( \lambda_{R} \right)}} \right)}\end{matrix}}.}} & (5)\end{matrix}$5. Note that, in discrete time, the following approximation can be made:

$\begin{matrix}{\frac{{\mathbb{d}\log}\mspace{11mu}{I\left( {\lambda,t} \right)}}{\mathbb{d}t} \cong {{\log\mspace{11mu} I\;\left( {\lambda,t_{2}} \right)} - {\log\mspace{11mu} I\;{\left( {\lambda,t_{1}} \right).}}}} & (6)\end{matrix}$6. Rewriting Eq. 6 yields the following:

$\begin{matrix}{\frac{{\mathbb{d}\log}\;{I\left( {\lambda,t} \right)}}{\mathbb{d}t} \cong {{\log\left( \frac{I\left( {\lambda,t_{2}} \right)}{I\left( {\lambda,t_{1}} \right)} \right)}.}} & (7)\end{matrix}$7. Thus, Eq. 4 can be expressed as follows:

$\begin{matrix}{{{\frac{\frac{{\mathbb{d}\log}\;{I\left( \lambda_{R} \right)}}{\mathbb{d}t}}{\frac{{\mathbb{d}\log}\;{I\left( {\lambda_{IR},t} \right)}}{\mathbb{d}t}} \cong \frac{\log\left( \frac{I\left( {\lambda_{R},t_{1}} \right)}{I\left( {\lambda_{R},t_{2}} \right)} \right)}{\log\left( \frac{I\left( {\lambda_{IR},t_{1}} \right)}{I\left( {\lambda_{IR},t_{2}} \right)} \right)}} = R},} & (8)\end{matrix}$where R represents the “ratio of ratios.”8. Solving Eq. 4 for s using the relationship of Eq. 5 yields:

$\begin{matrix}{s = {\frac{{\beta_{r}\left( \lambda_{R} \right)} - {R\;{\beta_{r}\left( \lambda_{IR} \right)}}}{{R\left( {{\beta_{0}\left( \lambda_{IR} \right)} - {\beta_{r}\left( \lambda_{IR} \right)}} \right)} - {\beta_{0}\left( \lambda_{R} \right)} + {\beta_{r}\left( \lambda_{R} \right)}}.}} & (9)\end{matrix}$9. From Eq. 8, R can be calculated using two points (e.g., PPG maximumand minimum), or a family of points. One method applies a family ofpoints to a modified version of Eq. 8. Using the following relationship:

$\begin{matrix}{{\frac{{\mathbb{d}\log}\; I}{\mathbb{d}t} = \frac{{dI}/{dt}}{I}},} & (10)\end{matrix}$Eq. 8 becomes

$\begin{matrix}{\frac{\frac{{\mathbb{d}\log}\;{I\left( \lambda_{R} \right)}}{\mathbb{d}t}}{\frac{{\mathbb{d}\log}\;{I\left( {\lambda_{IR},t} \right)}}{\mathbb{d}t}} \cong \frac{\frac{{I\left( {\lambda_{R},t_{2}} \right)} - {I\left( {\lambda_{R},t_{1}} \right)}}{I\left( {\lambda_{R},t_{1}} \right)}}{\frac{{I\left( {\lambda_{IR},t_{2}} \right)} - {I\left( {\lambda_{IR},t_{1}} \right)}}{I\left( {\lambda_{IR},t_{1}} \right)}}} \\{= \frac{\left( {{I\left( {\lambda_{R},t_{2}} \right)} - {I\left( {\lambda_{R},t_{1}} \right)}} \right){I\left( {\lambda_{IR},t_{1}} \right)}}{\left( {{I\left( {\lambda_{IR},t_{2}} \right)} - {I\left( {\lambda_{IR},t_{1}} \right)}} \right){I\left( {\lambda_{R},t_{1}} \right)}}} \\{{= R},}\end{matrix}$which defines a cluster of points whose slope of y versus x will give Rwhenx=(I(λ_(IR) ,t ₂)−I(λ_(IR) ,t ₁))I(λ_(R) ,t ₁),  (12)andy=(I(λ_(R) ,t ₂)−I(λ_(R) ,t ₁))I(λ_(IR) ,t ₁).  (13)Once R is determined or estimated, for example, using the techniquesdescribed above, the blood oxygen saturation can be determined orestimated using any suitable technique for relating a blood oxygensaturation value to R. For example, blood oxygen saturation can bedetermined from empirical data that may be indexed by values of R,and/or it may be determined from curve fitting and/or otherinterpolative techniques.

Pulse rate can be determined from the IR light signal, the Red lightsignal, any other suitable wavelength light signal, or a combination oflight signals.

FIG. 1 is a perspective view of an embodiment of a physiologicalmonitoring system 10, which may be used to implement a rate algorithm,for example. System 10 may include sensor unit 12 and monitor 14. Insome embodiments, sensor unit 12 may be part of an oximeter. Sensor unit12 may include an emitter 16 for emitting light at one or morewavelengths into a subject's tissue. A detector 18 may also be providedin sensor 12 for detecting the light originally from emitter 16 thatemanates from the subject's tissue after passing through the tissue. Anysuitable physical configuration of emitter 16 and detector 18 may beused. In an embodiment, sensor unit 12 may include multiple emittersand/or detectors, which may be spaced apart. System 10 may also includeone or more additional sensor units (not shown) which may take the formof any of the embodiments described herein with reference to sensor unit12. An additional sensor unit may be the same type of sensor unit assensor unit 12, or a different sensor unit type than sensor unit 12.Multiple sensor units may be capable of being positioned at twodifferent locations on a subject's body; for example, a first sensorunit may be positioned on a subject's forehead, while a second sensorunit may be positioned at a subject's fingertip.

Sensor units may each detect any signal that carries information about asubject's physiological state, such as arterial line measurements or thepulsatile force exerted on the walls of an artery using, for example,oscillometric methods with a piezoelectric transducer. According toanother embodiment, system 10 may include a plurality of sensors forminga sensor array in lieu of either or both of the sensor units. Each ofthe sensors of a sensor array may be a complementary metal oxidesemiconductor (CMOS) sensor. Alternatively, each sensor of an array maybe a charged coupled device (CCD) sensor. In some embodiments, a sensorarray may be made up of a combination of CMOS and CCD sensors. The CCDsensor may comprise a photoactive region and a transmission region forreceiving and transmitting data whereas the CMOS sensor may be made upof an integrated circuit having an array of pixel sensors. In someembodiments, each pixel may have a photodetector and an activeamplifier. In some embodiments, a group of pixels may share anamplifier. It will be understood that any type of sensor, including anytype of physiological sensor, may be used in one or more sensor units inaccordance with the systems and techniques disclosed herein. It isunderstood that any number of sensors measuring any number ofphysiological signals may be used to determine physiological informationin accordance with the techniques described herein.

In some embodiments, emitter 16 and detector 18 may be on opposite sidesof a digit such as a finger or toe, in which case the light that isemanating from the tissue has passed completely through the digit. In anembodiment, emitter 16 and detector 18 may be arranged so that lightfrom emitter 16 penetrates the tissue and is attenuated by the tissueand transmitted to detector 18, such as in a sensor designed to obtainpulse oximetry data from a subject's forehead.

In some embodiments, sensor unit 12 may be connected to and draw itspower from monitor 14 as shown. In another embodiment, the sensor may bewirelessly connected to monitor 14 and include its own battery orsimilar power supply (not shown). Monitor 14 may be configured tocalculate physiological parameters (e.g., pulse rate, blood pressure,blood oxygen saturation) based on data relating to light emission anddetection received from one or more sensor units such as sensor unit 12.In an alternative embodiment, the calculations may be performed on thesensor units or an intermediate device and the result of thecalculations may be passed to monitor 14. Further, monitor 14 mayinclude a display 20 configured to display the physiological parametersor other information about the system. In the embodiment shown, monitor14 may also include a speaker 22 to provide an audible sound that may beused in various other embodiments, such as for example, sounding anaudible alarm in the event that a subject's physiological parameters arenot within a predefined normal range. In some embodiments, the monitor14 includes a blood pressure monitor. In some embodiments, the system 10includes a stand-alone blood pressure monitor in communication with themonitor 14 via a cable or a wireless network link.

In some embodiments, sensor unit 12 may be communicatively coupled tomonitor 14 via a cable 24. In some embodiments, a wireless transmissiondevice (not shown) or the like may be used instead of or in addition tocable 24.

In the illustrated embodiment, system 10 includes a multi-parameterphysiological monitor 26. The monitor 26 may include a cathode ray tubedisplay, a flat panel display (as shown by display 28) such as a liquidcrystal display (LCD) or a plasma display, or may include any other typeof monitor now known or later developed. Multi-parameter physiologicalmonitor 26 may be configured to calculate physiological parameters andto provide a display 28 for information from monitor 14 and from othermedical monitoring devices or systems (not shown). For example,multi-parameter physiological monitor 26 may be configured to displaypulse rate information from monitor 14, an estimate of a subject's bloodoxygen saturation generated by monitor 14 (referred to as an “SpO₂”measurement), and blood pressure from monitor 14 on display 28.Multi-parameter physiological monitor 26 may include a speaker 30.

Monitor 14 may be communicatively coupled to multi-parameterphysiological monitor 26 via a cable 32 or 34 that is coupled to asensor input port or a digital communications port, respectively and/ormay communicate wirelessly (not shown). In addition, monitor 14 and/ormulti-parameter physiological monitor 26 may be coupled to a network toenable the sharing of information with servers or other workstations(not shown). Monitor 14 and/or multi-parameter physiological monitor 26may be powered by a battery (not shown) or by a conventional powersource such as a wall outlet. In some embodiments, monitor 14, monitor26, or both, may include one or more communications ports (not shown inFIG. 1) such as, for example, universal serial bus (USB) ports, ethernetports, WIFI transmitters/receivers, RS232 ports, any other suitablecommunications ports, or any combination thereof. In some embodiments,monitor 14, monitor 26, or both, may include memory (not shown inFIG. 1) such as, for example, a hard disk, flash memory (e.g., amultimedia card (MMC), a Secure Digital (SD) card), read only memory,any other suitable memory, any suitable communications ports forcommunicating with memory (e.g., a USB port for excepting flash memorydrives, an Ethernet port for communicating with a remote server), or anycombination thereof.

FIG. 2 is a block diagram of a physiological monitoring system, such asphysiological monitoring system 10 of FIG. 1, which may be coupled to asubject 40 in accordance with some embodiments. Certain illustrativecomponents of sensor unit 12 and monitor 14 are illustrated in FIG. 2.

Sensor unit 12 may include emitter 16, detector 18, and encoder 42. Inthe embodiment shown, emitter 16 may be configured to emit at least twowavelengths of light (e.g., Red and IR) into a subject's tissue 40.Hence, emitter 16 may include a Red light emitting light source such asRed light emitting diode (LED) 44 and an IR light emitting light sourcesuch as IR LED 46 for emitting light into the subject's tissue 40 at thewavelengths used to calculate the subject's physiological parameters. Insome embodiments, the Red wavelength may be between about 600 nm andabout 700 nm, and the IR wavelength may be between about 800 nm andabout 1000 nm. In some embodiments, in which a sensor array is used inplace of a single sensor, each sensor may be configured to emit a singlewavelength. For example, a first sensor emits only a Red light while asecond emits only an IR light. In another example, the wavelengths oflight used are selected based on the specific location of the sensor.

It will be understood that, as used herein, the term “light” may referto energy produced by radiation sources and may include one or more ofradio, microwave, millimeter wave, infrared, visible, ultraviolet, gammaray or X-ray electromagnetic radiation. As used herein, light may alsoinclude electromagnetic radiation having any wavelength within theradio, microwave, infrared, visible, ultraviolet, or X-ray spectra, andthat any suitable wavelength of electromagnetic radiation may beappropriate for use with the present techniques. Detector 18 may bechosen to be specifically sensitive to the chosen targeted energyspectrum of the emitter 16, the hemoglobin absorption profile, or both.

In some embodiments, detector 18 may be configured to detect theintensity of light at the Red and IR wavelengths. Alternatively, eachsensor in the array may be configured to detect intensity at a singlewavelength. In operation, light may enter detector 18 after beingattenuated (e.g., absorbed, scattered) by the subject's tissue 40.Detector 18 may convert the intensity of the received light into anelectrical signal. The light intensity is related to the absorptionand/or reflection of light in the tissue 40. That is, when more light ata certain wavelength is absorbed or reflected, less or more light ofthat wavelength is received from the tissue by the detector 18. Afterconverting the received light to an electrical signal, detector 18 maysend the signal to monitor 14, where physiological parameters may becalculated based on the absorption of the Red and IR wavelengths in thesubject's tissue 40.

In some embodiments, encoder 42 may contain information about sensor 12,such as sensor type (e.g., whether the sensor is intended for placementon a forehead or digit), the wavelengths of light emitted by emitter 16,power requirements or limitations of emitter 16, or other suitableinformation. This information may be used by monitor 14 to selectappropriate algorithms, lookup tables and/or calibration coefficientsstored in monitor 14 for calculating the subject's physiologicalparameters.

In some embodiments, encoder 42 may contain information specific tosubject 40, such as, for example, the subject's age, weight, anddiagnosis. Information regarding a subject's characteristics may allowmonitor 14 to determine, for example, subject-specific threshold rangesin which the subject's physiological parameter measurements should falland to enable or disable additional physiological parameter algorithms.This information may also be used to select and provide coefficients forequations from which, for example, pulse rate, blood pressure, and othermeasurements may be determined based on the signal or signals receivedat sensor unit 12. For example, some pulse oximetry sensors rely onequations to relate an area under a portion of a PPG signalcorresponding to a physiological pulse to determine blood pressure.These equations may contain coefficients that depend upon a subject'sphysiological characteristics as stored in encoder 42. Encoder 42 may,for instance, be a coded resistor which stores values corresponding tothe type of sensor unit 12 or the type of each sensor in the sensorarray, the wavelengths of light emitted by emitter 16 on each sensor ofthe sensor array, and/or the subject's characteristics. In someembodiments, encoder 42 may include a memory on which one or more of thefollowing information may be stored for communication to monitor 14: thetype of the sensor unit 12; the wavelengths of light emitted by emitter16; the particular wavelength each sensor in the sensor array ismonitoring; a signal threshold for each sensor in the sensor array; anyother suitable information; or any combination thereof. In someembodiments, encoder 42 may include an identifying component such as,for example, a radio-frequency identification (RFID) tag that may beread by decoder 74.

In some embodiments, signals from detector 18 and encoder 42 may betransmitted to monitor 14. In the embodiment shown, monitor 14 mayinclude a general-purpose microprocessor 48, FPGA 49, or both, connectedto an internal bus 50. In some embodiments, monitor 14 may include oneor more microprocessors, digital signal processors (DSPs), or both.Microprocessor 48 may be adapted to execute software, which may includean operating system and one or more applications, as part of performingthe functions described herein. Also connected to bus 50 may be aread-only memory (ROM) 52, a random access memory (RAM) 54, removablememory 53, user inputs 56, display 20, and speaker 22.

RAM 54, ROM 52, and removable memory 53 are illustrated by way ofexample (e.g., communications interface 90, flash memory, digital logicarray, field programmable gate array (FPGA), or any other suitablememory), and not limitation. Any suitable computer-readable media may beused in the system for data storage. Computer-readable media are capableof storing information that can be interpreted by microprocessor 48,FPGA 49, or both. This information may be data or may take the form ofcomputer-executable instructions, such as software applications, thatcause the microprocessor to perform certain functions and/orcomputer-implemented methods. Depending on the embodiment, suchcomputer-readable media may include computer storage media andcommunication media. Computer storage media may include volatile andnon-volatile, writable and non-writable, and removable and non-removablemedia implemented in any method or technology for storage of informationsuch as computer-readable instructions, data structures, program modulesor other data. Computer storage media may include, but is not limitedto, RAM, ROM, EPROM, EEPROM, flash memory or other solid state memorytechnology, CD-ROM, DVD, or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can be accessed by components of the system.

In the embodiment shown, a time processing unit (TPU) 58 may providetiming control signals to light drive circuitry 60, which may controlwhen emitter 16 is illuminated and multiplexed timing for Red LED 44 andIR LED 46. TPU 58 may also control the gating-in of signals fromdetector 18 through amplifier 62 and switching circuit 64. These signalsare sampled at the proper time, depending upon which light source isilluminated. In some embodiments, microprocessor 48, FPGA 49, or both,may de-multiplex the signal from detector 18 using de-multiplexingtechniques such as time-division, frequency-division, code division, orany other suitable de-multiplexing technique. In some embodiments,microprocessor 48, FPGA 49, or both, may perform the functions of TPU 58using suitable timing signals and multiplexing/de-multiplexingalgorithms, and accordingly TPU 58 need not be included. The receivedsignal from detector 18 may be passed through amplifier 66, low passfilter 68, and analog-to-digital converter 70. The digital data may thenbe stored in a queued serial module (QSM) 72 (or buffer such as a firstin first out (FIFO) buffer) for later downloading to RAM 54 as QSM 72fills up. A window of data may be selected from the data stored in thebuffer for further processing. In some embodiments, there may bemultiple separate parallel paths having components equivalent toamplifier 62, switching circuit 64, amplifier 66, filter 68, and/or A/Dconverter 70 for multiple light wavelengths or spectra received. In someembodiments, a filter (e.g., an analog filter) may be included (notshown) between amplifier 62 and switching circuit 64.

In an embodiment, microprocessor 48 may determine the subject'sphysiological parameters, such as pulse rate, SpO₂, and/or bloodpressure, using various algorithms and/or look-up tables based on thevalue of the received signals and/or data corresponding to the lightreceived by detector 18. Signals corresponding to information aboutsubject 40, and particularly about the intensity of attenuated lightemanating from a subject's tissue over time, may be transmitted fromencoder 42 to decoder 74. These signals may include, for example,encoded information relating to subject characteristics. Decoder 74 maytranslate these signals to enable the microprocessor to determine thethresholds based on algorithms or look-up tables stored in ROM 52. Insome embodiments, user inputs 56 may be used enter information, selectone or more options, provide a response, input settings, any othersuitable inputting function, or any combination thereof. User inputs 56may be used to enter information about the subject, such as age, weight,height, diagnosis, medications, treatments, and so forth. In someembodiments, display 20 may exhibit a list of values which may generallyapply to the subject, such as, for example, age ranges or medicationfamilies, which the user may select using user inputs 56.

Calibration device 80, which may be powered by monitor 14 via a coupling82, a battery, or by a conventional power source such as a wall outlet,may include any suitable signal calibration device. Calibration device80 may be communicatively coupled to monitor 14 via communicativecoupling 82, and/or may communicate wirelessly (not shown). In someembodiments, calibration device 80 is completely integrated withinmonitor 14. In some embodiments, calibration device 80 may include amanual input device (not shown) used by an operator to manually inputreference signal measurements obtained from some other source (e.g., anexternal invasive or non-invasive physiological measurement system).Calibration device 80 may be coupled to one or more components ofmonitor 14 to calibrate monitor 14.

Communications (“Comm”) interface 90 may include any suitable hardware,software, or both, which may allow physiological monitoring system 10(e.g., monitor 14, monitor 26) to communicate with electronic circuitry,a device, a network, or any combinations thereof. Communicationsinterface 90 may include one or more receivers, transmitters,transceivers, antennas, plug-in connectors, ports, communications buses,communications protocols, device identification protocols, any othersuitable hardware or software, or any combination thereof.Communications interface 90 may be configured to allow wiredcommunication (e.g., using USB, RS-232 or other standards), wirelesscommunication (e.g., using WiFi, IR, WiMax, BLUETOOTH, UWB, or otherstandards), or both. For example, communications interface 90 may beconfigured using a universal serial bus (USB) protocol (e.g., USB 1.0,USB 2.0, USB 3.0), and may be configured to couple to other devices(e.g., remote memory devices storing templates) using a four-pin USBstandard Type-A connector (e.g., plug and/or socket) and cable. In afurther example, communications interface 90 may be configured to accessa database server, which may contain a template database. In someembodiments, communications interface 90 may include an internal bussuch as, for example, one or more slots for insertion of expansion cards(e.g., to expand the capabilities of monitor 14, monitor 26, or both).

As described above, the optical signal attenuated by the tissue can bedegraded by noise, among other sources, and an electrical signal derivedthereof can also be degraded by noise. One source of noise is ambientlight that reaches the light detector. Another source of noise in anintensity signal is electromagnetic coupling from other electronicinstruments. Movement of the subject also introduces noise and affectsthe signal. For example, the contact between the detector and the skin,or the emitter and the skin, can be temporarily disrupted when movementcauses either to move away from the skin. In addition, because blood isa fluid, it responds differently than the surrounding tissue to inertialeffects, thus resulting in momentary changes in volume at the point towhich the oximeter probe is attached.

Noise (e.g., from subject movement) can degrade a sensor signal reliedupon by a care provider, without the care provider's awareness. This isespecially true if the monitoring of the subject is remote, the motionis too small to be observed, or the care provider is watching theinstrument or other parts of the subject, and not the sensor site.Analog and/or digital processing of sensor signals (e.g., PPG signals)may involve operations that reduce the amount of noise present in thesignals or otherwise identify noise components in order to prevent themfrom affecting measurements of physiological parameters derived from thesensor signals.

It will be understood that the present disclosure is applicable to anysuitable signal and that PPG signals are used merely for illustrativepurposes. Those skilled in the art will recognize that the presentdisclosure has wide applicability to other signals including, but notlimited to, other biosignals (e.g., electrocardiograms,electroencephalograms, electrogastrograms, electromyograms, pulse ratesignals, pathological signals, ultrasound signals, any other suitablebiosignals), or any combination thereof.

FIG. 3 is an illustrative signal processing system 300 in accordancewith some embodiments that may implement the signal processingtechniques described herein. In some embodiments, signal processingsystem 300 may be included in a physiological monitoring system (e.g.,physiological monitoring system 10 of FIGS. 1-2). In the illustratedembodiment, input signal generator 310 generates an input signal 316. Asillustrated, input signal generator 310 may include pre-processor 320coupled to sensor 318, which may provide input signal 316. In someembodiments, input signal 316 may include one or more intensity signalsbased on a detector output. In some embodiments, pre-processor 320 maybe an oximeter and input signal 316 may be a PPG signal. In anembodiment, pre-processor 320 may be any suitable signal processingdevice and input signal 316 may include PPG signals and one or moreother physiological signals, such as an electrocardiogram (ECG) signal.It will be understood that input signal generator 310 may include anysuitable signal source, signal generating data, signal generatingequipment, or any combination thereof to produce signal 316. Signal 316may be a single signal, or may be multiple signals transmitted over asingle pathway or multiple pathways.

Pre-processor 320 may apply one or more signal processing operations tothe signal generated by sensor 318. For example, pre-processor 320 mayapply a pre-determined set of processing operations to the signalprovided by sensor 318 to produce input signal 316 that can beappropriately interpreted by processor 312, such as performing A/Dconversion. In some embodiments, A/D conversion may be performed byprocessor 312. Pre-processor 320 may also perform any of the followingoperations on the signal provided by sensor 318: reshaping the signalfor transmission, multiplexing the signal, modulating the signal ontocarrier signals, compressing the signal, encoding the signal, andfiltering the signal. In some embodiments, pre-processor 320 may includea current-to-voltage converter (e.g., to convert a photocurrent into avoltage), an amplifier, a filter, and A/D converter, a de-multiplexer,any other suitable pre-processing components, or any combinationthereof.

In some embodiments, signal 316 may include PPG signals corresponding toone or more light frequencies, such as an IR PPG signal and a Red PPGsignal. In some embodiments, signal 316 may include signals measured atone or more sites on a subject's body, for example, a subject's finger,toe, ear, arm, or any other body site. In some embodiments, signal 316may include multiple types of signals (e.g., one or more of an ECGsignal, an EEG signal, an acoustic signal, an optical signal, a signalrepresenting a blood pressure, and a signal representing a heart rate).Signal 316 may be any suitable biosignal or any other suitable signal.

In some embodiments, signal 316 may be coupled to processor 312.Processor 312 may be any suitable software, firmware, hardware, orcombination thereof for processing signal 316. For example, processor312 may include one or more hardware processors (e.g., integratedcircuits), one or more software modules, computer-readable media such asmemory, firmware, or any combination thereof. Processor 312 may, forexample, be a computer or may be one or more chips (i.e., integratedcircuits). Processor 312 may, for example, include an assembly of analogelectronic components. Processor 312 may calculate physiologicalinformation. For example, processor 312 may compute one or more of apulse rate, respiration rate, blood pressure, oxygen saturation, or anyother suitable physiological parameter. Processor 312 may perform anysuitable signal processing of signal 316 to filter signal 316, such asany suitable band-pass filtering, adaptive filtering, closed-loopfiltering, any of the filtering disclosed herein, any other suitablefiltering, and/or any combination thereof. Processor 312 may alsoreceive input signals from additional sources (not shown). For example,processor 312 may receive an input signal containing information abouttreatments provided to the subject. Additional input signals may be usedby processor 312 in any of the calculations or operations it performs inaccordance with processing system 300.

In some embodiments, all or some of pre-processor 320, processor 312, orboth, may be referred to collectively as processing equipment. In someembodiments, any of the processing components and/or circuits, orportions thereof, of FIGS. 1-3 may be referred to collectively asprocessing equipment. For example, processing equipment may beconfigured to amplify, filter, sample and digitize input signal 316(e.g., using an analog to digital converter), and calculatephysiological information from the digitized signal. Accordingly, system300 may be used to implement a rate algorithm. In some embodiments, allor some of the components of the processing equipment may referred to asa processing module.

Processor 312 may be coupled to one or more memory devices (not shown)or incorporate one or more memory devices such as any suitable volatilememory device (e.g., RAM, registers, etc.), non-volatile memory device(e.g., ROM, EPROM, magnetic storage device, optical storage device,flash memory, etc.), or both. The memory may be used by processor 312to, for example, store fiducial information or initializationinformation corresponding to physiological monitoring. In someembodiments, processor 312 may store physiological measurements orpreviously received data from signal 316 in a memory device for laterretrieval. In some embodiments, processor 312 may store calculatedvalues, such as a pulse rate, a blood pressure, a blood oxygensaturation, a fiducial point location or characteristic, aninitialization parameter, or any other calculated values, in a memorydevice for later retrieval.

Processor 312 may be coupled to output 314. Output 314 may be anysuitable output device such as one or more medical devices (e.g., amedical monitor that displays various physiological parameters, amedical alarm, or any other suitable medical device that either displaysphysiological parameters or uses the output of processor 312 as aninput), one or more display devices (e.g., monitor, PDA, mobile phone,any other suitable display device, or any combination thereof), one ormore audio devices, one or more memory devices (e.g., hard disk drive,flash memory, RAM, optical disk, any other suitable memory device, orany combination thereof), one or more printing devices, any othersuitable output device, or any combination thereof.

It will be understood that system 300 may be incorporated into system 10(FIGS. 1 and 2) in which, for example, input signal generator 310 may beimplemented as part of sensor unit 12 (of FIGS. 1 and 2) and monitor 14(of FIGS. 1 and 2) and processor 312 may be implemented as part ofmonitor 14 (FIGS. 1 and 2). In some embodiments, portions of system 300may be configured to be portable. For example, all or part of system 300may be embedded in a small, compact object carried with or attached tothe subject (e.g., a watch, other piece of jewelry, or a smart phone).In some embodiments, a wireless transceiver (not shown) may also beincluded in system 300 to enable wireless communication with othercomponents of system 10 (FIGS. 1 and 2). As such, system 10 (FIGS. 1 and2) may be part of a fully portable and continuous subject monitoringsolution. In some embodiments, a wireless transceiver (not shown) mayalso be included in system 300 to enable wireless communication withother components of system 10. For example, pre-processor 320 may outputsignal 316 over BLUETOOTH, 802.11, WiFi, WiMax, cable, satellite,Infrared, or any other suitable transmission scheme. In someembodiments, a wireless transmission scheme may be used between anycommunicating components of system 300. In some embodiments, system 300may include one or more communicatively coupled modules configured toperform particular tasks. In some embodiments, system 300 may beincluded as a module communicatively coupled to one or more othermodules.

Pre-processor 320 or processor 312 may determine rate based on aperiodicity within physiological signal 316 (e.g., a PPG signal) that isassociated with a subject's pulse rate using one or more processingtechniques. For ease of illustration, the following rate determinationtechniques will be described as performed by processor 312, but anysuitable processing device (e.g., pre-processor 320, microprocessor 48,any other suitable components of system 10 and/or system 300, or anycombination thereof) may be used to implement any of the techniquesdescribed herein.

Physiological information such as pulse rate may be determined based onsignals received from a physiological sensor. FIG. 4 is a flow diagram400 of illustrative steps for determining physiological information of asubject, in accordance with some embodiments of the present disclosure.In accordance with flow diagram 400, an algorithm may be used tocondition and analyze a window of data buffered from a physiologicalsignal, and by determine a physiological rate when one or morequalification tests are passed. One or more settings of the algorithmmay be managed using, for example, a mode selection that may be used todefine the signal conditioning, qualification, and rate postingmanagement. The illustrative steps of flow diagram 400, or suitableportions thereof, will be referred to as the “rate algorithm” herein.

The steps of flow diagram 400, and all subsequent flow diagrams of thisdisclosure, may be performed using the physiological monitoring system10 of FIGS. 1-2, system 300 of FIG. 3, any other suitable system, or anycombination thereof. For example, in some embodiments, the steps may beperformed by a particular central processing unit (CPU) of physiologicalmonitoring system 10 (e.g., including microprocessor 48, bus 50, and anyor all components coupled to bus 50). In a further example (not shown),physiological monitoring system 10 may be a modular system, includingone or more functional modules (i.e., software, hardware, or acombination of both) configured to perform particular tasks or portionsof tasks thereof. Any suitable arrangement of physiological monitoringsystem 10, any other suitable system, or any combination thereof, may beused in accordance with the present disclosure. For illustrativepurposes, the flow diagrams of the present disclosure will be discussedin reference to processing equipment, which may include physiologicalmonitoring system 10, system 300 of FIG. 3, any other suitable system,any suitable components thereof, or any combination thereof.

Step 402 may include processing equipment initializing the algorithm fordetermining physiological information. In some embodiments, step 402 mayinclude beginning to fill the buffer with physiological data from thephysiological signal. In some embodiments, step 402 may includeinitializing one or more status flags or other algorithm settings.

Step 404 may include processing equipment managing one or more statusflags. In some embodiments, the processing equipment may initialize oneor more status flags, determine whether to change the value of one ormore status flags, change the value of one or more status flags, updateone or more status flags, receive information regarding one or morestatus flags, perform any other status flag management functions, or anycombination thereof. Status flags may include a pulse lost flag, asensor off flag, a gain change flag, a no valid saturation flag, aninitialization flag, a dropout flag, a mode flag, any other suitablestatus flag, or any combination thereof. Status flags may assume anysuitable indicator value such as, for example, a number (e.g., one orzero, or a positive integer), a letter (e.g., A, B, C), a text string(e.g., “pulse detected” or “pulse not detected”), any other suitableindicator, or any combination thereof. For example, the processingequipment may determine that no sensor is detected, and accordingly mayset the value of a sensor lost flag to one. If the processing equipmentsubsequently detects the sensor, the processing equipment may set thevalue of the sensor off flag to zero.

Step 406 may include processing equipment adding a current sample fromthe physiological signal to a memory buffer (a “buffer” such as QSM 72of system 10). In some embodiments the current sample may replace asample previously stored in the buffer, replace a place-holder value inthe buffer (e.g., a padding zero), add to a set of other stored values,or otherwise be stored in the memory buffer. In some embodiments, theprocessing equipment may add the current sample to the buffer, and ifthe buffer is not filled, the processing equipment may add one or moreplaceholder values to the buffer. For example, in some embodiments, theprocessing equipment may pad the buffer with zeros so the buffer doesnot have a reduced number of samples. In a further example, theprocessing equipment may pad the buffer with initialization values suchas suitably scaled random values so the buffer does not have a reducednumber of samples.

Step 408 may include processing equipment determining whether acalculation interval has been reached. In some embodiments, thecalculation interval may include a predetermined number of samples, orcorresponding time interval (e.g., 1 second corresponding to 57 samplesat a sampling rate of 57 Hz). For example, when the buffer has beenfilled with a particular number of samples from the physiologicalsignal, the calculation interval may be reached and the algorithm mayaccordingly proceed to step 410. In some embodiments, the processingequipment may determine the elapsed time since the last ratecalculation, or determine the number of additional samples added to thebuffer since the last rate calculation, and then determine whether toproceed to step 410, or repeat steps 404 and 406 before proceeding tostep 410.

Step 410 may include processing equipment managing one or more algorithmsettings. In some embodiments, algorithm settings may include anoperating mode, a flag setting, a threshold setting, a posting setting,a filter setting, any other suitable setting, or any combinationthereof. In some embodiments, step 410 may include determining a signalclassification, determining a signal metric such as a de-trend metric ornoise metric, performing any other classification or determination thatmay be used to affect the rate algorithm processing, or any combinationthereof. For example, the processing equipment may manage the algorithmsettings based on the value of a mode flag.

Step 412 may include processing equipment performing signal conditioningon the window of data stored in the buffer. Signal conditioning mayinclude applying a filter (e.g., a low pass, high pass, band pass,notch, or any other suitable analog or digital filter), amplifying,performing an operation on the received signal (e.g., taking aderivative, averaging), applying a derivative limiter, performingnormalization, performing a geometrical de-trending (e.g., of anysuitable type), applying a finite impulse response (FIR) filter,performing any other suitable signal conditioning, or any combinationthereof. For example, in some embodiments, step 406 may include removinga DC offset, removing low frequency components, removing high frequencycomponents, performing a mean subtraction or other baseline subtraction,performing any other suitable signal conditioning, or any combinationthereof. In a further example, step 406 may include smoothing thereceived physiological signal (e.g., using a moving average or othersmoothing technique). In some embodiments, the type of signalconditioning performed at step 412 may depend on one or more algorithmsettings (e.g., managed at step 410).

Step 414 may include processing equipment performing a correlation usingthe conditioned data of step 412. The correlation may include anautocorrelation, partial autocorrelation, cross-correlation, any othersuitable correlation, or any combination thereof. In some embodiments,the correlation may include a discrete correlation, as shown in Eq. 14:

$\begin{matrix}{{A_{xx}(j)} = {\sum\limits_{n = 0}^{N - 1}\;{x_{n}x_{n - j}}}} & (14)\end{matrix}$in which a discrete correlation coefficient A_(xx) may be computed for Nsamples x_(n) for a range of lag values indexed by j, respectively. Thecorrelation output may include a sequence of points, and may be referredto as a correlation sequence. The term autocorrelation, as used herein,shall also refer to a partial autocorrelation. For example, the termautocorrelation may be used to describe a correlation between segmentsof a given window of data, whether or not the segments share any datapoints. Accordingly, as used herein, the term autocorrelation may beused to describe a correlation between segments of a window of datasharing zero points, all points, or some points. The termcross-correlation, as used herein, shall refer to a correlation betweentwo sets of data points not included in the same window of data. In someembodiments, the first portion of data, the second portion of data, orboth, may be padded with zeros (e.g., at either or both ends of theportion) to equate the lengths of the first and second portions to aidin performing an autocorrelation.

In some embodiments, the correlation of step 414 may include correlatinga first segment of the window of data with a second segment of thewindow of data. The first and second segments may be exclusive of oneanother, may share one or more samples, or may be selected by any othersuitable partition of the window of data. For example, referencing a sixsecond window, the most recent three seconds of data may be correlatedwith the entire six seconds of data to produce a correlation sequence.In a further example, referencing a six second window, the most recentthree seconds of data may be correlated with the previous three secondsof data, so that the segments do not overlap. In some embodiments, theremay be a time gap between the first and second segments. For example,the most recent one second of data may be correlated with previous datanot immediately preceding the one second of data.

The correlation output of step 414 may include a sequence of datapoints, indexed by lag values, and may exhibit one or more peaks,troughs, or both. Peaks may be associated with relatively highcorrelation, zeros may be associated with relatively low correlation,and troughs may be associated with relatively high anti-correlation. Lagvalues corresponding to peaks may indicate time intervals correspondingto a period of a physiological pulse rate, or a multiple thereof (e.g.,when the subject's pulse rate is relatively constant).

Step 416 may include processing equipment determining a correlation lagbased on the correlation output of step 414. In some embodiments, theprocessing equipment may identify one or more peaks of the correlationoutput of step 414, and accordingly determine one or more lag valuesassociated with each of the one or more peaks. For example, in someembodiments, the processing equipment may generate one or morethresholds, and may determine the correlation lag corresponding to anythreshold crossings. In a further example, the processing equipment mayidentify a maximum value in the correlation output, and determine thelag corresponding to the maximum. The correlation lag may be determinedin units of sample point shifts (e.g., a lag of 10 points), timeinterval (e.g., a lag of 1 second), any other suitable lag units, or anycombination thereof.

Step 418 may include processing equipment qualifying a determinedcorrelation lag of step 416 by applying one or more qualification tests.Step 418 may include calculating one or more qualification metricsindicative of an estimated quality of the determined correlation lagvalue. For example, one or more correlation lag values may be determinedat step 416, using any suitable technique of the present disclosure.Some illustrative techniques of step 418, referred to as “QualificationTechniques”, will be described in further detail herein during thediscussion of FIGS. 86-126 of the present disclosure.

Step 420 may include processing equipment determining whether the one ormore qualification tests of step 418 have passed. If the correlation lagvalue of step 416 is determined to be qualified, then the processingequipment may proceed to calculate a rate at step 422. If thecorrelation lag value of step 416 is determined to be disqualified(e.g., exhibit a low confidence value), then the processing equipmentmay skip step 422 and proceed directly to step 424 to manage rateposting. In some embodiments, if the correlation lag value of step 416is determined to be disqualified, then the processing equipment mayupdate one or more counters (e.g., a dropout counter) and/or one or morestatus flags (e.g., a Dropout Status Flag) at step 404. In someembodiments, the processing equipment may determine whether one or morequalification tests have passed based on a qualification metric, athreshold value, a look-up table, any other suitable information, or anycombination thereof.

In some situations, the processing equipment may not be able todetermine a lag in step 416. For example, when there is a large amountof noise in the physiological signal, there may not be a peak in thecorrelation that exceeds the threshold. When a lag is not determined,the processing equipment may treat it as though the lag qualificationsfailed and proceed to step 424 to manage rate posting.

Step 422 may include processing equipment calculating a physiologicalrate. In some embodiments, the processing equipment may determine aphysiological rate by identifying the correlation lag value anddetermining the rate having a characteristic period equal to thecorrelation lag value.

Step 424 may include processing equipment managing posting of thecalculated rate of step 422. In some embodiments, the processingequipment may filter the calculated rate, and may output the filteredrate for display (e.g., on display 20 of physiological monitoring system10) at step 424. For example, step 424 may include low pass filtering ofthe calculated rate to limit the rate of change of the outputted rate tophysiological ranges. In a further example, step 424 may includeapplying an infinite impulse response (IIR) filter to the calculatedrate of step 422. In a further example, step 424 may include applying afinite impulse response (FIR) filter to the calculated rate of step 422.In some embodiments, step 424 may include storing the filtered ratevalues in memory such as, for example, RAM 54 or other suitable memoryof physiological monitoring system 10. If the qualification was notpassed at step 420, the processing equipment may continue to post theprevious rate, or no rate, at step 424.

Step 426 may include processing equipment preparing for a subsequentiteration of the algorithm. In some embodiments, step 426 may includeadjusting one or more algorithm settings, setting one or more statusflags, or both.

In an illustrative example, the processing equipment may implement thetechniques of flow diagram 400 using three operating modes, which may bedesignated using a Mode Status Flag. During startup, or in the event ofa dropout, the rate algorithm may operate in Mode 1. While in Mode 1,the processing equipment may perform rate calculations, yet may not posta rate at all. However, Mode 1 operation may include relatively strictqualification criteria to prevent noise tracking. When a particularnumber of lags have been qualified, the rate algorithm may set the ModeStatus Flag to Mode 2, and begin posting rates. If calculated rates aredisqualified, the rate algorithm may return to Mode 1, while if asufficient number of calculated rates are qualified, the rate algorithmmay proceed to Mode 3 and apply a bandpass filter to the physiologicaldata.

Further details and implementations of the present disclosure, includingfurther details and implementations of flow diagram 400, are discussedbelow.

FIG. 5 is a flow diagram 500 of illustrative steps for initializing atechnique for determining physiological information, in accordance withsome embodiments of the present disclosure. Initialization techniquesmay be desired in circumstances where a limited amount of physiologicaldata may be available and/or desired (e.g., during the start of datacollection). In some embodiments, Initialization allows system 300 orsystem 10 to start processing a physiological signal before an entirebuffer (e.g., 6 or 7 seconds of data) is obtained from the physiologicalsignal. An entire buffer of physiological data (e.g., 6 seconds in thisexample, although an entire buffer may be any suitable length), forexample, is typically only needed to accurately determine rates down to20 BPM. At 20 BPM, a 6 second buffer will include 2 periods worth ofdata. Since most rates are 50 BPM or higher, it is possible for system300 or system 10 to begin processing physiological data before 6 secondsof data have been obtained and still accurately determine rate. In someembodiments, a fixed buffer size may be used and this may not requireany modification of the algorithm. For example, Initialization may allowsubsequent processing such as, for example, a correlation calculation tobe performed without adjusting a template size. In a further example, a3 second window of data may still be used in the correlationcalculation, and the portions of the window not yet filled withphysiological data may be filled with initialization data (e.g., noise).The initialization data may be expected to roughly cancel out. Theillustrative techniques may be performed as part of steps 402, 404, 406,408, any other suitable processing steps of flow diagram 400 or othersuitable steps, or any combination thereof. In some embodiments,Initialization may be only used once and the initialization values maywork their way out of the buffer as new physiological data is received.In some embodiments, Initialization may be used until sufficientphysiological data is received to fill the buffer, and then thealgorithm may perform steps 404-426 as needed, without repeating step402. In some embodiments, Initialization (e.g., step 402 of flow diagram400 and as described by the illustrative technique of flow diagram 500)may be followed by step 410. In some embodiments, an Initialization Flagmay be set to one when sufficient samples of physiological data are notavailable, indicating that initialization techniques are to used (e.g.,as described in the context of flow diagram 500).

Step 502 may include the processing equipment receiving one or moresamples of physiological data, derived from a physiological signal. Step502 may include pre-processing (e.g., using pre-processor 320) theoutput of a physiological sensor, and then storing a window of theprocessed data in any suitable memory or buffer (e.g., QSM 72 of system10), for further processing by the processing equipment. In someembodiments, the window of data may be recalled from data stored inmemory (e.g., RAM 54 of FIG. 2 or other suitable memory) for subsequentprocessing. The number of samples of physiological data received duringInitialization may be relatively smaller than the preferred buffer sizeof physiological data during normal operation.

Step 504 may include processing equipment determining one or moreinitialization values based on the one or more received samples. Step506 may include processing equipment generating a window of data thatincludes the one or more initialization values, the one or more receivedsamples of physiological samples, or any suitable combination thereof.Initialization values may be used to fill the buffer when sufficientphysiological data is not available (e.g., allowing a fixed buffer sizeto be used regardless of the amount of physiological data available). Insome embodiments, the one or more initialization values may be generatedby adding random values (e.g., noise) to the one or more receivedsamples. For example, if a single sample of physiological data isavailable, the remaining buffer may be filled with samples generated byadding random values, scaled and shifted according to the receivedsample. As shown in the following Eq. 15a:[values]=sample*(1+K([RAND]−0.5))  (15a)an array of N initialization values [values] may be generated bygenerating an array of N random numbers [RAND] between zero and one,subtraction 0.5 to set the expected mean to zero, scaling by a factor K(e.g., such as 0.01 or other suitable number), and adding to thereceived sample. Note that the subtraction of 0.5 may cause someinitialization values to be greater than the value of the receivedsample, and some initialization values to be less than the value of thereceived sample. In a further example, where multiple samples ofphysiological data are received, Eq. 15a may be used, with the samplevalue replaced by an average value (or other suitable representativevalue derived from the multiple samples), and the factor K replaced witha standard deviation (or other variation metric derived from themultiple samples). In a further example, where multiple samples ofphysiological data are received, initialization values may be generatedusing Eq. 15a for each of the received samples, in which the samplevalue and the factor K are taken from one or more received samples andused to generate one or more initialization values. In some embodiments,the random numbers may be generated in real-time or may be predeterminedrandom numbers to minimize unpredictability. In a further example, theprocessing equipment may apply Eq. 15b:[values]=V*(1+C*N)  (15b)to generate one or more initialization values [values] from a value Vbased on at least one sample (e.g., a sample value, a sample valueaverage), a coefficient C, and a noise value N (e.g., a standarddeviation value derived from a physiological signal, a noise metric).Any suitable technique may be used to fill the remaining buffer withdetermined initialization values based on one or more received samplesof physiological data. The portion of the buffer filled with randomnumbers may roughly cancel or may have a relatively small impact duringsubsequent correlation calculations.

Step 508 may include the processing equipment proceeding with processingthe window of data of step 506. In some embodiments, the window of dataof step 506 may be analyzed using any of the techniques of steps 410-426of flow diagram 400 of FIG. 4. For example, the window of data, whichmay include one or more initialization values, may be conditioned (e.g.,using any suitable Signal Conditioning Technique).

In an illustrative example, the algorithm may require at least a secondof physiological data to determine a physiological rate, and accordinglythe first second's worth of data may be used to determine how to fillthe remaining portion of the buffer with initialization values.Referencing a six second buffer, once the first second of physiologicaldata is obtained during Initialization, the processing equipment mayfill the buffer with the one second of physiological data and fiveseconds of initialization values. For example, one or more suitableproperties of the first second of data (e.g., average, standarddeviation, maximum, minimum) may be used to determine initializationvalues for the remaining portion of the buffer. The algorithm mayproceed to step 404 (e.g., where the status flags may set to skip step406), and then to steps 410-426, until another second of physiologicaldata is available. As more physiological data becomes available, it maydisplace the initialization values from the buffer, until the buffer isfilled completely with physiological data and no initialization values.In some embodiments, Initialization is completed after theinitialization values are determined based on a first portion of thephysiological data. In some embodiments, Initialization continues untilthe buffer is completed filled with physiological data. For example, theprocessing equipment may perform steps 404-426 and then obtainadditional physiological data and determine updated initializationvalues based on the additional data. Once the buffer is completelyfilled with physiological data, the processing equipment may performsteps 404-426 without performing another Initialization at step 402. Inany of the embodiments, suitable steps of the algorithm of flow diagram400 may be performed multiple times during Initialization, until a fullwindow of physiological data is available to fill the buffer. Forexample, a rate may be calculated at step 422 each second while thebuffer is partially filled with initialization values.

In some embodiments, the algorithm may use a relatively smaller bufferduring startup. For example, the algorithm may use a buffer size of 4seconds during startup rather than six seconds. Further, the algorithmmay use a template size of 2 seconds to generate an correlation sequenceat step 414. In some embodiments, the algorithm may, for example,transition to a six second buffer after the first rate has posted. Somesuch techniques, described as Fast Start are described below. Fast Startmay be used in circumstances where a reduced-size window of data may beavailable and/or desired. In some embodiments, Fast Start allows system300 or system 10 to start processing a physiological signal before anentire buffer (e.g., 6 or 7 seconds of data) is obtained. Fast Start maybe especially useful during start-up, and/or start of data collection.An entire buffer of data (e.g., 6 seconds in this example, although anentire buffer may be any suitable length), for example, is typicallyonly needed to accurately compute rates down to 20 BPM. Since most ratesare 60 BPM or higher, the rate algorithm can begin determininginformation sooner. Fast start parameters may include buffer sizes of aphysiological signal, templates sizes, an increment of increase inbuffer size and/or template size, a time and/or available buffer size toend Fast Start, correlation analysis parameters, signal conditioningparameters, qualification parameters, any other suitable parameters, orany combination thereof. For example, the processing equipment maydetermine a starting buffer and correlation template size (e.g., a twosecond buffer and a one second template). In a further example, theprocessing equipment may determine how to increment the buffer andtemplate size as more data is available and/or desired (e.g., increasethe buffer size by one second for each calculation with the correlationtemplate being a fixed proportion of the buffer). In a further example,the processing equipment may determine that when a six second buffer ofdata is available, the rate algorithm may transition out of Fast Startmode and begin normal operation. In a further example, the processingequipment may determine signal conditioning parameters such as curve fitsubtraction parameters. In some embodiments, the processing equipmentmay determine whether to operate in Fast Start mode.

FIG. 6 is a table 600 of illustrative status flags, in accordance withsome embodiments of the present disclosure. Status flags may include apulse lost flag, a sensor lost flag, a gain change flag, a no validsaturation flag, an initialization flag, a dropout flag, a mode flag,any other suitable status flag, or any combination thereof. Status flagsmay assume any suitable indicator value such as, for example, a number(e.g., one or zero, or a positive integer), a letter (e.g., A, B, C), atext string (e.g., “pulse detected” or “pulse not detected”), any othersuitable indicator, or any combination thereof. The rate algorithm mayuse status flags, for example, to aid in determining or otherwisemanaging algorithm settings. FIG. 7 is a block diagram of illustrativememory 700 including rate algorithm information, in accordance with someembodiments of the present disclosure. Memory 700 may store status flagdata 710, algorithm settings 720, algorithm routines 730, buffer 740,physiological data 750, any other suitable information 760, or anycombination thereof. In the illustrated embodiment, status flag data 710includes status flag data structure 712, which includes an array of flagindicators (e.g., numerical values, letters, text strings), although anyother suitable data structure, or any combination thereof may be used inaccordance with the present disclosure. Any suitable number of statusflags may be stored in memory, and accordingly, the processing equipmentmay update any of the status flags as desired. Algorithm settings 720may include threshold values, switch settings, parameter values, anyother suitable settings, or any combination thereof. Algorithm routines730 may include sets of computer readable instructions, executablefunctions, any other computer code, or any combination thereof. Buffer740 may include a current interval of physiological data from aphysiological signal, an interval of sequentially calculated values, anyother suitable set or sets of values, or any combination thereof.Previous data 750 may include historical physiological data, historicalcalculated values, any other suitable information determined or receivedpreviously, or any combination thereof. Other information 760 mayinclude references such as look-up tables, databases, any other suitableinformation that may be used by the rate algorithm, or any combinationthereof.

In some embodiments, a Pulse Lost Status Flag and/or Sensor Off StatusFlag may be used. If either of these flags are received, the ratealgorithm may be stopped (e.g., rate calculation may stop), althoughdata may continue to be added to the buffer, optionally. The algorithmmay be restarted when a flag is received indicating that either or bothof these flags have been cleared. In some embodiments, the processingequipment may set a Pulse Lost Status Flag based on a calculated rate(e.g., the rate is outside of an expected physiological range), a signalconditioning metric (e.g., a determined noise metric based on thephysiological signal), a result of a Qualification test (e.g., adisqualified rate), an output of a separate module configured to detectwhen the pulse is lost, any other suitable information, or anycombination thereof.

In some embodiments, a Gain Change Status Flag may be used. When a gainchange occurs, an artifact may be introduced into the physiologicalsignal. The artifact may include, for example, a baseline change, dampedoscillations that dissipate out after a few seconds, or other features.For example, during servoing, when LED power and/or amplifier gainsettings are adjusted, the physiological signal may exhibit gainchanges. The rate algorithm may continue to add the data to the buffer,but not calculate a rate for a predetermined time interval (orcorresponding sample interval). For example, the rate algorithm may bepaused until the artifact has passed through the buffer (e.g., displacedby physiological data received after the artifact). In a furtherexample, the buffer may be reinitialized once the artifact is over(e.g., using any suitable Initialization Technique), and the baselinehas settled. In a further example, the rate algorithm may freeze thebuffer until the artifact is over and then continue filling the buffer.To minimize potential discontinuities in the data, the rate algorithmmay smooth or filter the signal from before and after the artifact,freeze the buffer for a period of time that is an integer multiple ofthe period of a previously calculated rate, perform any other suitableprocessing to minimize the potential discontinuities, or perform anycombination thereof.

In some embodiments, a No Valid Saturation Status Flag may be used. Insome circumstances, where an oxygen saturation module is not able tocalculate a valid oxygen saturation value, the processing equipment mayset the No Valid Saturation Status Flag. In some embodiments, duringthese flagged conditions, the rate algorithm may continue unaffected.However, the No Valid Saturation Status Flag may indicate that the rateis wrong (e.g., when rate information feeds into a saturationcalculation for filtering or any other purpose). In some embodiments,the rate algorithm may be reinitialized (e.g., using any suitableInitialization Technique), or may perform other suitable checks (e.g.,using any suitable Qualification Technique) to confirm the correct rateis being calculated. In some embodiments, rate may continue to becalculated during a No Valid Saturation Status Flag, but not posted.

In some embodiments, an Initialization Status Flag may be used. In someembodiments, the processing equipment may set the Initialization StatusFlag during startup of the rate algorithm. For example, as the buffer isfilled with physiological data, the Initialization Status Flag may beset. In a further example, the processing equipment may set theInitialization Status Flag prior to a rate calculation being performed.In some embodiments, the rate algorithm may set the InitializationStatus Flag if the Dropout Status Flag is set. In some embodiments, therate algorithm may release the Initialization Status Flag when a ratehas been calculated, qualified, or both.

In some embodiments, a Dropout Status Flag may be used. In someembodiments, the processing equipment may set the Dropout Status Flagwhen one or more calculated rates are disqualified. The rate algorithmmay use the Dropout Status Flag, for example, to prevent locking on tonoise in the physiological signal. When the Dropout Status Flag is set,the rate algorithm may clear all buffers and settings, and transition toa particular Mode.

In some embodiments, a Mode Status Flag may be used. In someembodiments, the processing equipment may set the Mode Status Flagdepending upon which mode the rate algorithm is currently operating in,or is to operate in. The rate algorithm may, for example, change theMode Status Flag value based on any other suitable flag value, based onwhether a calculated rate is qualified or disqualified, based on ahistory of qualifications or disqualifications, based on predeterminedtime intervals, based on any other suitable criterion, or based on anycombination thereof.

FIG. 8 is a flow diagram 800 of illustrative steps for managing a statusindicator, in accordance with some embodiments of the presentdisclosure.

Step 802 may include the processing equipment receiving physiologicaldata, derived from a physiological signal. Step 802 may includepre-processing (e.g., using pre-processor 320) the output of aphysiological sensor, and then, at step 804, storing a window of thephysiological data in any suitable memory or buffer (e.g., QSM 72 ofsystem 10), for further processing by the processing equipment. In someembodiments, the window of data may be recalled from data stored inmemory (e.g., RAM 54 of FIG. 2 or other suitable memory) for subsequentprocessing.

Step 806 may include the processing equipment receiving or generating astatus indicator. In some embodiments, for example, the status indicatormay be a gain change indicator or Gain Change flag, set based onhardware gain changes. For example, a gain change indicator may be setby the processing equipment based on a controlled change inamplification (e.g., switching from 1× gain to 4× gain) of thephotodetector signal, a change in LED power (e.g., an increase ordecrease in supplied current to a RED and/or IR LED), or both. In someembodiments, the status indicator may be a Pulse Lost or Sensor Offstatus indicator. For example, if a PPG sensor becomes unplugged orotherwise inoperative, a Sensor Off status indicator may be set. Thestatus indicator may include any suitable numerical value, letter, textstring, symbol, or any combination thereof.

Step 808 may include the processing equipment setting a period of timeduring which physiological data are not added to the window of data. Insome embodiments, physiological data may be added to the window of data,but rate is not calculated for a predetermined time interval (e.g.,number of seconds, number of samples). For example, in response to astatus indicator, the processing equipment may cease from ratecalculation for a time interval equal to or greater than the length ofthe buffer, to allow any large signal changes to substantially passthrough the buffer. In some embodiments, the buffer may bere-initialized after a status indicator (e.g., an initialization flag isactivated), and the algorithm may proceed with a partial buffer similarto start-up. In some embodiments, the portion of data corresponding to atransient change due to, for example, a gain change or sensor offcondition, may be excluded from the window of data. For example, thebuffer may be frozen (i.e., no new data is added) until the transientchange artifact has passed, and the physiological data before and afterthe artifact may be joined (e.g., concatenated). Smoothing or othersuitable processing techniques may be applied in some such instances.

Step 810 may include the processing equipment proceeding with processingthe window of data of step 804 to determine a physiological parameter.In some embodiments, after the status indicator has been received andthe processing equipment has omitted physiological data corresponding tothe gain change artifact, the processing equipment may perform a RateCalculation.

FIG. 9 is a block diagram of illustrative techniques for managingalgorithm settings, in accordance with some embodiments of the presentdisclosure. The techniques may be implemented as part of managealgorithm settings step 410 of FIG. 4. The techniques may includeselecting an algorithm mode 904, selecting a signal classification 906,determining a signal conditioning metric 908, selecting rate filtersettings 910, selecting other settings 912, or any combination thereof.Selecting an algorithm mode 904 may include selecting the mode frommultiple modes based on one or more status flags, or other information.For example, upon startup, the processing equipment may select Mode 1.After one or more lags have been qualified, the processing equipment mayselect Mode 2. If, for example, the rate algorithm wants to turn on abandpass filter, which may be indicative of higher confidence in therate calculation, the processing equipment may transition to Mode 3.This is merely illustrative. Any suitable number of modes and modetransitions may be used. Selecting a signal classification 906 mayinclude selecting a signal classification from multiple classifications.For example, classifications may be based on subject age (e.g., neonate,adult), presence of a dicrotic notch, pulse wave shape, any othersuitable classifications, or any combination thereof. The signalclassification may be selected based on user input, one or moredetermined metrics, any other suitable information, or any combinationthereof. Determining a signal conditioning metric 908 may includedetermining a de-trend metric, a noise metric, any other suitable signalmetric, or any combination thereof. In some embodiments, the processingequipment may determine a signal conditioning metric to applyde-trending, reduce noise, reduce artifacts, reject a window of data, orother signal conditioning function. Selecting rate filter settings 910may include selecting a type of rate filter, a filter parameter orcoefficient value, any other rate filter type or rate filter setting, orany combination thereof. For example, the processing equipment mayselect a low pass filter, a high pass filter, a band pass filter, afinite impulse response (FIR) filter, an infinite impulse response (IIRfilter), any other suitable filter type, or any combination thereof.Further, the processing equipment may select the amount of filteringthat may be applied to physiological data. For example, the processingequipment may select a FIR filter to filter the posted rate value, and acorresponding set of filter coefficients (e.g., to weight the previousinput values for generating an output value). Selecting other settings912 may include selecting threshold values, count threshold values(e.g., for activating a Dropout status flag), posting settings (e.g.,whether to post rate values or not), qualification tests, any othersuitable settings, or any combination thereof.

FIG. 10 is a flow diagram 1000 of illustrative steps for managingalgorithm settings using a classification, in accordance with someembodiments of the present disclosure. Classification of physiologicaldata may aid in determining algorithm settings and/or calculating arate, by further directing the analysis of the physiological data. Forexample, filter settings and amount of filtering, expected pulse raterange, subject classification (e.g., neonate or adult), the presence ofa dicrotic notch, pulse shape (e.g., skew), and/or any other suitableclassification may be used to determine the type of analysis to performon physiological data from a physiological signal.

Step 1002 may include the processing equipment receiving physiologicaldata, derived from a physiological signal. Step 1002 may includepre-processing (e.g., using pre-processor 320) the output of aphysiological sensor, and then storing a window of the physiologicaldata in any suitable memory or buffer (e.g., QSM 72 of system 10), forfurther processing by the processing equipment. In some embodiments, thewindow of data may be recalled from data stored in memory (e.g., RAM 54of FIG. 2 or other suitable memory) for subsequent processing. Forexample, referring to system 300 of FIG. 3, the processing equipment mayreceive a physiological signal from input signal generator 310. Sensor318 of input signal generator 310 may be coupled to a subject, and maydetect physiological activity such as, for example, RED and/or IR lightattenuation by tissue, using a photodetector. In some embodiments,physiological signals generated by input signal generator 310 may bestored in memory (e.g., memory of system 10 of FIGS. 1-2) after beingpre-processed by pre-processor 320. In such cases, step 1002 may includerecalling the signals from the memory for further processing.

The physiological signal of step 1002 may include a PPG signal, whichmay include a sequence of pulse waves and may exhibit motion artifacts,noise from ambient light, electronic noise, system noise, any othersuitable signal component, or any combination thereof. Step 1002 mayinclude receiving a particular time interval or corresponding number ofsamples of the physiological signal. In some embodiments, step 1002 mayinclude receiving a digitized, sampled, and pre-processed physiologicalsignal.

Step 1004 may include the processing equipment determining one or moremetrics based on the physiological data of step 1002. The one or moremetrics may include de-trend metrics, noise metrics, any other suitablemetrics, or any combination thereof. For example, any of the metricsdescribed in the context of FIGS. 11-41 may be determined at step 1004.

Step 1006 may include the processing equipment classifying thephysiological data of step 1002 based on the one or more metrics of step1004. The processing equipment may perform the classification using anysuitable set of classes, which may be based on signal quality, signalproperties, subject properties, any other suitable types of classeshaving any suitable number of classes, or combination thereof.Illustrative classifications may include, for example, subject age(e.g., neonate/child/adult), high/low motion artifact (e.g., motion of asubjects limbs), dicrotic notch/no dicrotic notch, high/low pulseskewness, likely pulse rate range, signal noisiness, any other suitableclassification having any suitable number of classes, or any combinationthereof. In some embodiments, step 1006 may include the processingequipment receiving user input (e.g., to user inputs 56 of system 10).For example, a user may indicate that the received physiological data isfrom a neonate, does not have a dicrotic notch, and/or likely includes apulse rate in a particular range. In a further example, the skewness Sof n samples (e.g., corresponding to one or more pulse waves) may bedetermined using Eq. 16:

$\begin{matrix}{S = \frac{\frac{1}{n}\left( {\sum\limits_{i = 1}^{n}\;\left( {x_{i} - \overset{\_}{\mu}} \right)^{3}} \right)}{\left( {\frac{1}{n}\left( {\sum\limits_{i = 1}^{n}\;\left( {x_{i} - \overset{\_}{\mu}} \right)^{2}} \right)} \right)^{3/2}}} & (16)\end{matrix}$where μ is the sample mean, and x_(i) is sample i. In some embodiments,in which the processing equipment is unable to classify a physiologicalsignal and/or no user indication is received, the processing equipmentneed not classify the physiological signal and may proceed using any ofthe techniques disclosed herein.

Step 1008 may include the processing equipment determining one or morealgorithm settings based on the classification of step 1006. In someembodiments, the processing equipment may use the classification todetermine which Operating Mode to operate in. For example, if a PPGsignal is classified as a neonate PPG signal, the signal may be highpassed or band-passed to reduce or eliminate frequencies less than about85 BPM because neonates typically have rate higher than 100 BPM. In afurther example, qualification tests to be performed may be changed, orthe thresholds may be changed, based on the classification. For example,if a PPG signal is classified as having a dicrotic notch, additional orrelatively more stringent tests may be applied to make sure the dicroticnotch is not causing the rate to be calculated as double the true rate.If double the true rate is detected, then the calculated rate may behalved and provided as an output. In some embodiments, algorithmsettings such as, for example, indexes for inputting into a look-uptable, filter settings, and/or templates may be determined. In someembodiments, one or more settings of a band pass filter such as, forexample, a high and low frequency cutoff value (e.g., a frequencyrange), a representative frequency value, a set of one or morecoefficients, any other suitable parameters, or any combination thereofmay be determined. Physiological monitoring system 10 may use determinedalgorithm settings to improve data processing (e.g., reducecomputational requirements, improve accuracy, reduce the effects ofnoise) to extract physiological information in the presence of noise.This may be accomplished by effectively limiting the bandwidth of datato be analyzed, performing a rough calculation to estimate aphysiological rate or pulse, or otherwise mathematically manipulatingphysiological data. In some embodiments, following a change inclassification, the processing equipment may determine that algorithmsettings are to be reset. In some embodiments, in which more than twoclasses exist, the processing equipment may determine whether algorithmsettings are to be reset based on the relative change in classification.For example, a physiological signal may be classified by noise level,and the processing equipment may determine whether algorithm settingsare to be adjusted depending on the change in noise level. In someembodiments, step 1008 may be independent of the classification of step1006.

In an illustrative example, a window of physiological data may beclassified as having a dicrotic notch. The processing equipment may,accordingly, determine that a calculated value (e.g., a correlation lagvalue, or value derived thereof) corresponds to a harmonic of thephysiological rate. In some such circumstances, the processing equipmentmay modify the calculated value to obtain the physiological rate whenthe classification of the physiological data is a dicrotic notchclassification. For example, if physiological data is classified ashaving a dicrotic notch, the processing equipment may determine that onehalf of the calculated value corresponds to the physiological rate.

Classification of the physiological data may be implemented by theprocessing equipment with the use of one or more metrics. The metricsand techniques discussed in the context of FIGS. 11-41 may be used toclassify physiological data based on a metric value, and set one or morealgorithm settings based on the classification. For example, if the ratealgorithm determines that the physiological data likely has a dicroticnotch, then the rate algorithm may determine not to apply an FIR filterbased on a weighted sum of the data and a difference signal derivedthereof (more details of such a filter are provided in the descriptionof FIG. 60). In a further example, if the rate algorithm cannotdetermine satisfactorily whether the physiological data likely has adicrotic notch, then the rate algorithm may also determine not to applyan FIR filter based on a weighted sum of the data and a differencesignal derived thereof. In a further example, an algorithm setting mayaffect the amount of filtering that is applied to the physiologicaldata. In some embodiments, particular metrics may be used to perform aparticular classification by the rate algorithm. For example, the ratealgorithm may use the metrics discussed in the context of FIGS. 13-28 toclassify physiological data as having a dicrotic notch or not. In afurther example, the rate algorithm may use the metrics discussed in thecontext of FIGS. 30-41 to classify physiological data based on adetermined level of noise. In a further example, the rate algorithm mayuse the metrics discussed in the context of FIGS. 11-41 to set one ormore algorithm settings such qualification tests performed,qualification requirements, filter settings, any other suitablesettings, or any combination thereof.

FIG. 11 is a flow diagram of illustrative steps for classifyingphysiological data, in accordance with some embodiments of the presentdisclosure. The steps of illustrative flow diagram 1100 may provide anexemplary embodiment of steps 1004 and 1006 of flow diagram 1000 of FIG.10. The steps of illustrative flow diagram 1100 may be used by the ratealgorithm, for example, to classify the physiological data based on therate (e.g., low rate, high rate), which may be used to determine one ormore algorithm settings (e.g., filter settings, de-trend settings).

Step 1102 may include processing equipment receiving physiological datafrom a physiological sensor, memory, any other suitable source, or anycombination thereof. For example, referring to system 300 of FIG. 3, theprocessing equipment may receive a window of physiological data frominput signal generator 310. Sensor 318 of input signal generator 310 maybe coupled to a subject, and may detect physiological activity such as,for example, RED and/or IR light attenuation by tissue, using aphotodetector. In some embodiments, physiological signals generated byinput signal generator 310 may be stored in memory (e.g., RAM 54 of FIG.2 or other suitable memory) after being pre-processed by pre-processor320. In such cases, step 1102 may include recalling data from the memoryfor further processing.

Step 1104 may include the processing equipment applying a filter such asa low-pass filter (LPF) or high-pass filter (HPF) to the receivedphysiological data of step 1102. In some embodiments, applying thefilter may include separating the physiological data into a lowfrequency component and a high frequency component (i.e., relativelyhigher frequency activity), as shown in FIG. 11. The processingequipment may apply a filter having any suitable spectral character(e.g., the LPF may be a Bessel filter, Chebyshev filter, ellipticfilter, Butterworth filter, or other suitable low-pass filter, havingany suitable spectral cutoff). For example, the processing equipment mayapply a LPF having a 75 BPM cut-off at step 1104 (e.g., which attenuatesfrequencies greater than approximately 75 BPM). In a further example,the processing equipment may apply a HPF having a 75 BPM cut-off at step1104 (e.g., which attenuates frequencies less than approximately 75BPM). The 75 BPM cut-off is exemplary and any other suitable BPM cutoffcan be used to separate the window of physiological data into high andlow frequency components.

Step 1106 may include the processing equipment determining a noisemetric based on the low frequency component outputted at step 1104. Insome embodiments, the processing equipment may apply a LPF (separatefrom the filter of step 1104) to the LF component, and then compare theinput and output signals to the second LPF. For example, step 1104 mayinclude applying a LPF with a cutoff of 75 BPM, and step 1106 mayinclude the processing equipment applying a second LPF with a cutoff of6 BPM. The processing equipment may determine the difference between theinput and output of the 6 BPM LPF, and then determine a root mean squarevalue of the difference as the noise metric.

Step 1108 may include the processing equipment calculating a kurtosis(e.g., the fourth standardized moment of a signal or corrected valuethereof) of the LF component outputted at step 1104. In someembodiments, for example, the processing equipment may calculate thekurtosis K for n data points using Eq. 17:

$\begin{matrix}{K = {\frac{\frac{1}{n}\left( {\sum\limits_{i = 1}^{n}\;\left( {x_{i} - \overset{\_}{\mu}} \right)^{4}} \right)}{\left( {\frac{1}{n}\left( {\sum\limits_{i = 1}^{n}\;\left( {x_{i} - \overset{\_}{\mu}} \right)^{2}} \right)} \right)^{2}} - 3}} & (17)\end{matrix}$where μ is the sample mean, and x_(i) is sample i. The kurtosis mayprovide an indication of a relative measure of sharpness of adistribution (e.g., high kurtosis indicates a relatively sharp peak andrelatively large tails).

Step 1110 may include the processing equipment calculating a standarddeviation of the LF component outputted at step 1104. In someembodiments, the processing equipment may calculate the standarddeviation σ for n data points using Eq. 18:

$\begin{matrix}{\sigma = \sqrt{\frac{1}{n}\left( {\sum\limits_{i = 1}^{n}\;\left( {x_{i} - \overset{\_}{\mu}} \right)^{2}} \right)}} & (18)\end{matrix}$where μ is the sample mean, and x_(i) is sample i. The standarddeviation may provide an indication of sample variability about a meanvalue.

Step 1112 may include the processing equipment calculating a kurtosis ofthe HF component outputted at step 1104. In some embodiments, theprocessing equipment may calculate the kurtosis K for n data pointsusing Eq. 17, in which the samples and mean are based on the HFcomponent. Step 1114 may include the processing equipment calculating astandard deviation of the HF component outputted at step 1104. In someembodiments, the processing equipment may calculate the standarddeviation σ for n data points using Eq. 18, in which the samples andmean are based on the HF component.

It will be understood that steps 1106, 1108, 1110, 1112, and 1114 may beperformed in any suitable order between steps 1104 and 1116.

Step 1116 may include the processing equipment performing comparisonanalysis between the LF component and the HF component outputted at step1104. In some embodiments, step 1116 may include the processingequipment comparing one or more signal metrics from the LF component andHF component outputted at step 1104. In some embodiments, at step 1116,the processing equipment may compare the kurtosis (e.g., from steps 1108and 1112), standard deviation (e.g., from steps 1110 and 1114), anyother suitable metric, or any combination thereof. For example, theprocessing equipment may determine which of the LF component and the HFcomponent has a larger kurtosis, standard deviation, and/or other metricvalue. In some embodiments, step 1116 may include the processingequipment performing independent component analysis (ICA) using the LFcomponent, the HF component, the physiological data of step 1102, or anycombination thereof. For example, ICA analysis may be used to separatethe component of physiological data associated with a physiological ratefrom noise components or other un-desired component of the data.

Step 1118 may include the processing equipment selecting either the LFcomponent or the HF component outputted at step 1104. In someembodiments, the processing equipment may select one component anddisregard the other, non-selected component. For example, the processingequipment may determine at step 1116 that the LF component has a higherkurtosis and/or standard deviation than the HF component, andaccordingly may select the LF component for further processing by therate algorithm.

FIG. 12 is a panel showing two plots of illustrative physiologicalsignals, one of which exhibits a dicrotic notch, in accordance with someembodiments of the present disclosure. Plots 1200 and 1250 showrespective difference signals 1202 and 1252 (e.g., differences orderivatives of adjacent samples suitably scaled) derived from respectivePPG signals (not shown, but detected as transmitted light so absorptionpeak upstroke is negative). Note that the difference signal 1202 isderived from a PPG signal of a neonate and exhibits no dicrotic notch,while difference signal 1252 is derived from a PPG signal of an adultand exhibits a dicrotic notch. Difference signal 1202 exhibits a seriesof peak/troughs of relatively similar size and shape, while differencesignal 1252 exhibits a series of peak/troughs of alternating size andshape. The presence of the dicrotic notch in the PPG signal associatedwith difference signal 1252 causes a large negative trough associatedwith the absorption upstroke, and a smaller negative trough associatedwith the upstroke immediately following the dicrotic notch. The metricsdiscussed in the context of FIGS. 13-28 may be used to quantify thedifferences between difference signals such as 1202 and 1252, andaccordingly classify physiological data. Accordingly, the processingequipment may use one or more metrics to distinguish physiological datahaving a dicrotic notch from physiological data that is either from aneonate or otherwise exhibits no dicrotic notch. In some embodiments,the classification of whether physiological data has a dicrotic notchmay be used to determine the type of de-trending applied to thephysiological data and the metrics may be referred to as de-trendingmetrics.

In some embodiments, one or more metrics may be used to determine one ormore algorithm settings. In some embodiments, de-trending metrics may beused to classify physiological data. For example, a de-trending metricmay be sensitive to the presence of a dicrotic notch in physiologicaldata, and the value of the de-trending metric may be used to classifythe physiological data. In some embodiments, a difference signal suchas, for example, a first derivative signal may be generated fromphysiological data. In some embodiments, the difference signal may besorted, and characteristics of the sorted difference signal may beanalyzed.

FIG. 13 is a flow diagram 1300 of illustrative steps for determining analgorithm setting based on an offset of positive and negative differencevalues, in accordance with some embodiments of the present disclosure.FIG. 14 is a panel showing two illustrative difference signals 1402 and1452 derived from respective physiological signals, one of whichexhibits a dicrotic notch, along with sorted positive and negativevalues 1472 and 1474, in accordance with some embodiments of the presentdisclosure. FIG. 14 will be referred to below during the discussion ofthe illustrative steps of flow diagram 1300.

Step 1302 may include processing equipment receiving physiological datafrom a physiological sensor, memory, any other suitable source, or anycombination thereof. For example, referring to system 300 of FIG. 3, theprocessing equipment may receive a window of physiological data frominput signal generator 310. Sensor 318 of input signal generator 310 maybe coupled to a subject, and may detect physiological activity such as,for example, RED and/or IR light attenuation by tissue, using aphotodetector. In some embodiments, physiological signals generated byinput signal generator 310 may be stored in memory (e.g., RAM 54 of FIG.2, QSM 72 and/or other suitable memory) after being pre-processed bypre-processor 320. In such cases, step 1302 may include recalling datafrom the memory for further processing.

Step 1304 may include processing equipment generating a differencesignal (e.g., by calculating a sequence of difference values betweenadjacent samples of the physiological data). In some embodiments, theprocessing equipment may perform a subtraction between values ofadjacent samples. In some embodiments, the processing equipment maycalculate the differences by calculating a first derivative of thephysiological data. For example, the processing equipment may computeforward differences, backward differences, or central differencesbetween each pair of adjacent points to generate a difference signal. Ina further example, the processing equipment may compute a numericalderivative at each point in the data, generating a difference signal.Any suitable difference technique may be used by the processingequipment to generate the difference signal.

Step 1306 may include processing equipment sorting the difference valuesof step 1304. The processing equipment may sort the values in ascendingor descending order, either of which causes the negative and positivevalues to be separated. Referencing sorted values in ascending order,the most negative values come first followed by less negative values,positive values, and finally larger positive values. Accordingly thesorted values can be separated into positive values and negative values,and the two sets of values may be processed separately.

Step 1308 may include processing equipment determining a midpoint valueof the positive values of the sorted difference signal. In someembodiments, the processing equipment may determine a median value ofthe sorted positive values. For example, referencing a vector of 101sorted positive values, the processing equipment may select the 51^(st)data point as the midpoint. For vectors having an even number datapoints, either of the two middle points may be selected, or acombination (e.g., an average of the two points) may be used.

Step 1310 may include processing equipment determining an offset valueof the positive values of the sorted difference signal. In someembodiments, the processing equipment may locate the offset value at aparticular relative location in the sorted positive values. For example,the processing equipment may select a data point at a particularlocation such as 16% from the end of the vector corresponding to thelargest positive values. In an illustrative example, referencing avector of 100 positive values sorted in ascending order, the offsetvalue may be selected as the 84^(th) value (e.g., 16% from the endcorresponding to the largest values). Any suitable vector location,absolute or relative, may be used to select the offset value, and the16% value is used merely for illustration.

Step 1312 may include processing equipment determining a differencebetween the positive midpoint value of step 1308 and the positive offsetvalue of step 1310. In some embodiments, the absolute value of thedifference may be determined. Alternatively, instead of or in additionto performing steps 1308-1312, the processing equipment may determine astandard deviation value of the positive values, and use the standarddeviation value as the difference value. Any suitable metric may be usedto represent the difference in the positive values.

Step 1314 may include processing equipment determining a midpoint valueof the negative values of the sorted difference signal. In someembodiments, the processing equipment may determine a median value ofthe sorted negative values. Step 1316 may include processing equipmentdetermining an offset value of the negative values of the sorteddifference signal. In an illustrative example, referencing a vector of100 negative values sorted in ascending order, the offset value may beselected as the 16^(th) value (e.g., 16% from the end corresponding tothe most negative values). Any suitable vector location, absolute orrelative, may be used to select the offset value, and the 16% value isused merely for illustration. Step 1318 may include processing equipmentdetermining a difference between the negative midpoint value of step1314 and the negative offset value of step 1316. In some embodiments,the absolute value of the difference may be determined. Alternatively,instead of or in addition to performing steps 1314-1318, the processingequipment may determine a standard deviation value of the negativevalues, and use the standard deviation value as the difference value.Any suitable metric may be used to represent the difference in thenegative values.

Step 1320 may include processing equipment determining a ratio of thepositive and negative differences of respective steps 1312 and 1318. Insome embodiments, the processing equipment may determine the ratio in afixed manner such as positive over negative, or negative over positive.In some embodiments, the processing equipment may determine the ratio asthe smaller value over the larger value, which normalizes the ratio tobetween zero and one. In some embodiments, the ratio may be determinedas a positive number, and accordingly the processing equipment maydetermine suitable absolute values.

Step 1322 may include processing equipment determining an algorithmsetting based on the determined ratio of step 1320. In some embodiments,the ratio may be compared with a threshold value. For example,referencing a ratio normalized to between zero and one, if the ratio isabove 0.75, then the processing equipment may determine that no dicroticnotch is present, and if the ratio is below 0.5, the processingequipment may determine that a dicrotic notch is likely present.Further, if the ratio is between 0.5 and 0.75, the processing equipmentcan refrain from classifying the data. Alternatively, the processingequipment may use a single threshold rather than two thresholds, and alldata may be classified as having a dicrotic notch or not. Accordingly,depending on the comparison of the ratio to the threshold, theprocessing equipment may classify the physiological data of step 1302,and set one or more algorithm settings. In some embodiments, theprocessing equipment may bias the classification towards one or theother (e.g., dicrotic notch or no dicrotic notch) depending on whatalgorithm setting is being set based on the metric. In an illustrativeexample, if the physiological data is classified as having a dicroticnotch, then the processing equipment may turn off or modify a FIR filterthat weights the data and a difference signal derived thereof to preventa double rate calculation. The presence of a dicrotic notch can causethe difference signal to appear as a double rate condition, with peaksbefore and after each notch appearing similar to separate pulses.

Referencing FIG. 14, plot 1400 shows difference signal 1402, derivedfrom a PPG signal having no dicrotic notch. Solid line 1406 correspondsto the median for the positive values of difference signal 1402, whiledashed lines 1404 and 1408 correspond to a ±1 standard deviation band(based on the positive values). Solid line 1412 corresponds to themedian for the negative values of difference signal 1402, while dashedlines 1410 and 1414 correspond to a ±1 standard deviation band (based onthe negative values). Note that the ±1 standard deviation bands for thepositive and negative values are roughly equal. Plot 1450 showsdifference signal 1452, derived from a PPG signal having a dicroticnotch. Solid line 1456 corresponds to the median for the positive valuesof difference signal 1452, while dashed lines 1454 and 1458 correspondto a ±1 standard deviation band (based on the positive values). Solidline 1462 corresponds the median for the negative values of differencesignal 1452, while dashed lines 1460 and 1464 correspond to a ±1standard deviation band (based on the negative values). Note that the ±1standard deviation bands for the positive and negative values aresignificantly different. The difference in positive and negativestandard deviation bands for difference signals 1402 and 1452illustrates some aspects of flow diagram 1300, and the quantification ofthe difference. For example, the differences between the midpoint valueand the offset value in steps 1312 and 1328 may be considered to be arough approximation of the standard deviation of the positive andnegative values. Plot 1400 illustrates that for a PPG signal having nodicrotic notch, the ratio determined in step 1320 may be expected to beclose to one (e.g., because the positive and negative standarddeviations bands are of similar size). Plot 1450 illustrates that for aPPG signal having a dicrotic notch, the ratio determined in step 1320may be expected to be significantly less than one (e.g., because thepositive and negative standard deviation bands are significantlydifferent).

Plot 1470 shows illustrative sorted difference signal 1472, derived froma difference signal corresponding to physiological data exhibiting adicrotic notch. Plot 1480 shows illustrative sorted difference signal1482, derived from a difference signal corresponding to physiologicaldata of a neonate. Differences between the sorted positive and negativevalues are apparent between difference signals 1472 and 1482. Forexample, the sorted negative values take different shapes for sorteddifference signals 1472 and 1482, due to the presence of the two-tiernegative peaks resulting from the dicrotic notch. Sorted differencesignal 1472 exhibits a “knee” (e.g., a bend in a curve between regionshaving two different characteristic slopes) in the negative valueportion, indicative of a dicrotic notch. The knee arises from thepresence of two sets of troughs, shallow and deep, in a differencesignal (e.g., as shown in plot 1450) which give rise to substantiallytwo sets of negative slope values on either side of the knee. Sorteddifference signal 1482 does not exhibit a knee in the negative valueportion, as there is not expected to be two distinct sets of troughswhen no dicrotic notch is present.

FIG. 15 is a flow diagram 1500 of illustrative steps for determining analgorithm setting based on a sorted difference signal, in accordancewith some embodiments of the present disclosure. FIG. 16 is a panelshowing a sorted difference signal and two histograms, in accordancewith some embodiments of the present disclosure. FIG. 16 will bereferred to below during the discussion of the illustrative steps offlow diagram 1500.

Step 1502 may include processing equipment receiving physiological datafrom a physiological sensor, memory, any other suitable source, or anycombination thereof. For example, referring to system 300 of FIG. 3, theprocessing equipment may receive a window of physiological data frominput signal generator 310. Sensor 318 of input signal generator 310 maybe coupled to a subject, and may detect physiological activity such as,for example, RED and/or IR light attenuation by tissue, using aphotodetector. In some embodiments, physiological signals generated byinput signal generator 310 may be stored in memory (e.g., RAM 54 of FIG.2, QSM 72 and/or other suitable memory) after being pre-processed bypre-processor 320. In such cases, step 1502 may include recalling datafrom the memory for further processing.

Step 1504 may include processing equipment generating a differencesignal (e.g., by calculating a sequence of difference values betweenadjacent samples of the physiological data). In some embodiments, theprocessing equipment may perform a subtraction between values ofadjacent samples. In some embodiments, the processing equipment maycalculate the differences by calculating a first derivative of thephysiological data. For example, the processing equipment may computeforward differences, backward differences, or central differencesbetween each pair of adjacent points to generate a difference signal. Ina further example, the processing equipment may compute a numericalderivative at each point in the data, generating a difference signal.Any suitable difference technique may be used by the processingequipment to generate the difference signal.

Step 1506 may include processing equipment sorting the difference valuesof step 1504. The processing equipment may sort the values in ascendingor descending order, either of which causes the negative and positivevalues to be separated. Referencing sorted values in ascending order,the most negative values come first followed by less negative values,positive values, and finally larger positive values. Accordingly thesorted values can be separated into positive values and negative values,and the two sets of values may be processed separately. Steps 1508-1514will refer to the “positive values” and the “negative values” of thesorted difference signal separately, although separation of the positiveand negative values into separate arrays is not necessarily required.Additionally, the positive values and the negative values will bereferred to as vectors (e.g., 1-D collections of values), although anysuitable data structure and representation thereof may be used inaccordance with the present disclosure. In some embodiments, thenegative values may be multiplied by −1, and the resulting values may bere-sorted in ascending order, for example.

Step 1508 may include processing equipment determining a set ofreference indices of the sorted difference signal. In some embodiments,the processing equipment may determine a median value of the sortedpositive values. For example, referencing a vector of 101 sortedpositive values, the processing equipment may select the 51^(st) datapoint at the midpoint. For vectors having an even number data points,either of the two middle points may be selected, or a combination (e.g.,an average of the two points) may be used. In some embodiments, theprocessing equipment may determine an offset value at a particularrelative location of a vector of sorted positive values or negativevalues. For example, for a vector of the positive values, the processingequipment may select a data point at a particular location such as 16%from the end (i.e., index 1) of the vector corresponding to the largestpositive values. In an illustrative example, referencing a vector of 100positive values sorted in ascending order, the offset value may beselected as the 84^(th) value (e.g., 16% from the end corresponding tothe largest values at index 100). Any suitable vector location, absoluteor relative, may be used to select the offset value, and the 16% valueis used merely for illustration. The set of reference indices mayinclude median values, offset values, or any combination thereof.

Step 1510 may include processing equipment generating at least onehistogram based on the reference indices and the positive and negativevalues of the sorted difference signal. In some embodiments, theprocessing equipment may use the reference indices to identify segmentsof the positive value vector and the negative value vector from which togenerate a histogram. For example, the processing equipment select thesegment of the positive value vector between first and second referenceindices, and the segment of the negative value vector between the firstand second reference indices. The processing equipment may generate ahistogram by determining the ratio of each value of positive valuevector with the corresponding value (e.g., having the same index) of thenegative value vector, to generate a vector of ratio values. Theprocessing equipment may generate a histogram based on the ratio values.In some embodiments, a second histogram may be generated similar to thefirst histogram, albeit using at least one different index of thereference indices than that used to generate the first histogram. Forexample, considering the example of the previous paragraph, theprocessing equipment may select the segment of the positive value vectorbetween second and third reference indices, and the segment of thenegative value vector between the second and third reference indices, togenerate the second histogram. Any suitable number of segments, andcorresponding histograms, may be used in accordance with the presentdisclosure.

Step 1512 may include processing equipment determining an algorithmsetting based on the at least one histogram of step 1510. In someembodiments, for example, the processing equipment may generate twohistograms at step 1510, determine the respective maximum values of thetwo histograms, and determine the algorithm setting based on the maximumvalues. In an illustrative example, the processing equipment may receivephysiological data and generate a sorted difference signal. Theprocessing equipment may partition the sorted difference signal into theset of positive values (e.g., the positive value vector) and the set ofnegative values (e.g., the negative value vector). The processingequipment may then multiply the negative values by −1, and re-sort,although the set of values will still be referred to here as the“negative values.” The processing equipment may then determine referenceindices of 16%, 50%, and 84% of the length of the shorter of the twovectors. The processing equipment may then generate a set of ratiovalues (e.g., a ratio value vector) of the ratio of each positive valueto the corresponding (e.g., the i^(th) value of the positive valuevector by the i^(th) value of the negative value vector) negative valuebetween the 16% and 50% indices. The processing equipment may thengenerate a histogram of the ratio values and select the maximum valueand the corresponding index (e.g., indexed relative to the segmentbetween the 16% and 50% indices). The processing equipment may thengenerate a second set of ratio values (e.g., a ratio value vector) ofthe ratio of each positive value to the corresponding (e.g., the i^(th)value of the positive value vector by the i^(th) value of the negativevalue vector) negative value between the 50% and 84% indices. Theprocessing equipment may then generate a second histogram of the ratiovalues and select the maximum value and the corresponding index (e.g.,indexed relative to the segment between the 50% and 84% indices). Basedon the maximum values, the processing equipment may classify thephysiological data of step 1502, set one or more algorithm settings,determine a value indicative of noise, or any combination thereof.

FIG. 16 is a panel showing a sorted difference signal and twohistograms, in accordance with some embodiments of the presentdisclosure. Plot 1600 shows sorted difference signal 1602. Plot 1610shows a histogram 1612 of values based on sorted difference signal 1602,while plot 1620 shows a magnified view of plot 1610. Based on likelybehavior, region 1614 of plot 1610 corresponds to values indicative of aphysiological signal, while region 1616 corresponds to values indicativeof noise. Accordingly, the rate algorithm may classify the physiologicaldata with respect to noise based on a histogram. For example, if aparticular fraction of a histogram lies in region 1616, then the ratealgorithm may classify the data as being noisy. Further based on likelybehavior, region 1622 of plot 1620 may correspond to values indicativeof a physiological signal exhibiting a dicrotic notch and a relativelysharp pulse, while region 1624 may correspond to values indicative of aneonate, and region 1626 may corresponds to values indicative of adistorted signal. Accordingly, the rate algorithm may classify thephysiological data with respect to the presence of a dicrotic notchbased on a histogram. For example, if a particular fraction of ahistogram lies in region 1622 or 1624, then the rate algorithm mayclassify the data as having a dicrotic notch or arising from a neonate,respectively.

FIG. 17 is a flow diagram 1700 of illustrative steps for determining analgorithm setting based on area ratios of positive and negative regionsof a difference signal, in accordance with some embodiments of thepresent disclosure. FIG. 18 is a panel showing two illustrative plots1800 and 1850 of respective difference signals 1802 and 1852 havingpositive and negative regions, in accordance with some embodiments ofthe present disclosure. FIG. 18 will be referred to below during thediscussion of the illustrative steps of flow diagram 1700.

Step 1702 may include processing equipment receiving physiological datafrom a physiological sensor, memory, any other suitable source, or anycombination thereof. For example, referring to system 300 of FIG. 3, theprocessing equipment may receive a window of physiological data frominput signal generator 310. Sensor 318 of input signal generator 310 maybe coupled to a subject, and may detect physiological activity such as,for example, RED and/or IR light attenuation by tissue, using aphotodetector. In some embodiments, physiological signals generated byinput signal generator 310 may be stored in memory (e.g., RAM 54 of FIG.2, QSM 72 and/or other suitable memory) after being pre-processed bypre-processor 320. In such cases, step 1702 may include recalling datafrom the memory for further processing.

Step 1704 may include processing equipment generating a differencesignal (e.g., by calculating a sequence of difference values betweenadjacent samples of the physiological data). In some embodiments, theprocessing equipment may perform a subtraction between values ofadjacent samples. In some embodiments, the processing equipment maycalculate the differences by calculating a first derivative of thephysiological data. For example, the processing equipment may computeforward differences, backward differences, or central differencesbetween each pair of adjacent points to generate a difference signal. Ina further example, the processing equipment may compute a numericalderivative at each point in the data, generating a difference signal.Any suitable difference technique may be used by the processingequipment to generate the difference signal.

Step 1706 may include processing equipment determining values indicativeof area for positive and negative regions of the difference signal. Thedifference signal of 1704 may include a sequence of peaks and troughscorresponding to physiological pulse, along with other components suchas noise, and may have exhibit oscillatory character. The peaks mayinclude positive values of the difference signal, while the troughs mayinclude negative values of the difference signal. Accordingly, the peaks(along with the zero line) define a positive region having an area abovethe ordinate axis, while the troughs (along with the zero line) define anegative region having an area below the ordinate axis. Referencing plot1800 of FIG. 18, difference signal 1802, derived from a PPG signalexhibiting no dicrotic notch, exhibits a sequence of positive regions1804 and negative regions 1806. Difference signal 1852 of plot 1850,derived from a PPG signal exhibiting a dicrotic notch, exhibits asequence of positive regions 1854 and negative regions 1856. Valuesindicative of area may be determined for each positive and negativeregion, as indicated by the hatching in FIG. 18. The values indicativeof an area of a region may include a numerical integral (e.g., anysuitable quadrature such as the Trapezoid rule or Simpson's Rule),analytic integral (e.g., integral of a function fit of a region and thezero line), a summation of values of the region, a rectangular areacorresponding to the width and height of each region, any other areametric, or any combination thereof.

Step 1708 may include processing equipment determining area ratios ofadjacent positive and negative regions. In some embodiments, theprocessing equipment may determine an area ratio of each positive regionto the immediately following negative region, generating a sequence ofratio values. In some embodiments, the processing equipment maydetermine an area ratio of each negative region to the immediatelyfollowing positive region, generating a sequence of ratio values. Anysuitable technique may be used to determine area ratios between adjacentpositive and negative regions. For example, referencing plot 1800, theratio of the area of positive region 1820 and the area of negativeregion 1822 may be determined. It can be seen from plot 1800 that theratio of areas of positive regions to adjacent negative regions isroughly similar, although some deviation is present. Referencing plot1850, the ratio of the area of positive region 1860 and the area ofnegative region 1862 may be determined, and the ratio of the area ofpositive region 1870 and the area of negative region 1872 may bedetermined. It can be seen from plot 1850 that the ratio of areas ofpositive regions to adjacent negative regions will result in atwo-tiered set of values, caused by the alternating small and largeareas of the negative regions. The output of step 1708 may be a vectorof area ratio values derived from the difference signal of step 1704. Insome embodiments, the processing equipment may normalize the ratios tospan a predetermined range, such 0-1, for example. In some embodiments,the processing equipment may determine the ratio of negative regions topositive regions, in which case the analysis may be altered.

Step 1710 may include processing equipment determining an algorithmsetting based on the area ratios of step 1708. In some embodiments, theprocessing equipment may analyze a sequence of ratio values to determinea metric. For example, the 25% largest value (e.g., larger than about75% of ratio values and smaller than about 25% of ratio values) of asequence of ratio values of positive areas to adjacent negative areasmay be compared to a threshold. In a further example, the ratiocorresponding to the largest 25% of a sequence of ratio values ofpositive areas to adjacent negative areas may be compared to a thresholdvalue. In a further example, the average of the largest 25% of asequence of ratio values of positive areas to adjacent negative areasmay be compared to a threshold value. The ratio is expected to be near 1for data not exhibiting dicrotic notches. For data exhibiting dicroticnotches, the ratios may exhibit two tiers due to the two-tiered shapedof troughs in the difference signal. A first tier will be somewhat closeto 1, while the second tier will be significantly larger than 1. Bypicking the 25% value, the processing equipment will likely pick a valuein the middle of second tier for dicrotic notches and therefore be highwhen dicrotic notches are present. However, when dicrotic notches arenot present, the selected value is likely close to 1 because the areasratios are all generally close to 1 (provided noise is sufficientlylow). In some embodiments, the processing equipment may normalize thedetermined ratios, sort the normalized ratios into a sorted array, andselect the value at one fourth of the length of the array of sortedratios. In some embodiments, the processing equipment may compare the25% value to the 75% value. For data exhibiting a dicrotic notch the 25%and 75% values should each lie in the middle of the two tiers, while fordata not exhibiting a dicrotic notch, the values may be expected to berelatively similar.

FIG. 19 is a flow diagram 1900 of illustrative steps for determining analgorithm setting based on first and second difference signals, inaccordance with some embodiments of the present disclosure. FIG. 20 is apanel showing illustrative PPG signals with and without a dicroticnotch, and corresponding first and second difference signals for each,in accordance with some embodiments of the present disclosure. FIG. 20will be referred to below during the discussion of the illustrativesteps of flow diagram 1900.

Step 1902 may include processing equipment receiving physiological datafrom a physiological sensor, memory, any other suitable source, or anycombination thereof. For example, referring to system 300 of FIG. 3, theprocessing equipment may receive a window of physiological data frominput signal generator 310. Sensor 318 of input signal generator 310 maybe coupled to a subject, and may detect physiological activity such as,for example, RED and/or IR light attenuation by tissue, using aphotodetector. In some embodiments, physiological signals generated byinput signal generator 310 may be stored in memory (e.g., RAM 54 of FIG.2, QSM 72 and/or other suitable memory) after being pre-processed bypre-processor 320. In such cases, step 1902 may include recalling datafrom the memory for further processing.

Step 1904 may include processing equipment generating a first differencesignal (e.g., by calculating a sequence of difference values betweenadjacent samples of the physiological data). In some embodiments, theprocessing equipment may perform a subtraction between values ofadjacent samples. In some embodiments, the processing equipment maycalculate the differences by calculating a first derivative of thephysiological data. For example, the processing equipment may computeforward differences, backward differences, or central differencesbetween each pair of adjacent points to generate a first differencesignal. In a further example, the processing equipment may compute anumerical derivative at each point in the data, generating a firstdifference signal. Any suitable difference technique may be used by theprocessing equipment to generate the first difference signal.

Step 1906 may include processing equipment sorting the difference valuesof step 1904. The processing equipment may sort the values in ascendingor descending order. Referencing sorted values in ascending order, themost negative values come first followed by less negative values,positive values, and finally larger positive values.

Step 1908 may include processing equipment generating a seconddifference signal by calculating a sequence of difference values betweenadjacent samples of the negative values of the sorted first differencesignal of step 1906. In some embodiments, the processing equipment mayperform a subtraction between values of adjacent data points of thenegative values of the sorted first difference signal. In someembodiments, the processing equipment may calculate the differences bycalculating a first derivative of the sorted first difference signal.For example, the processing equipment may compute forward differences,backward differences, or central differences between each pair ofadjacent points of the sorted first difference signal to generate asecond difference signal. In a further example, the processing equipmentmay compute a numerical derivative at each point of the negative valuesof the sorted first difference signal, generating a second differencesignal. Any suitable difference technique may be used by the processingequipment to generate the second difference signal based on the negativevalues of the sorted first difference signal of step 1906.

Referencing FIG. 20, plot 2000 shows PPG signal 2002 (taken using an IRLED), which does not exhibit a dicrotic notch. Plot 2010 shows a sortedfirst difference signal 2012 derived from PPG signal 2002, includingonly the negative values. Plot 2020 shows a second difference signal2022 derived from sorted difference signal 2012. Plot 2050 shows PPGsignal 2052 (taken using an IR LED), exhibiting a dicrotic notch. Plot2060 shows a sorted first difference signal 2062 derived from PPG signal2052, including only the negative values. Plot 2070 shows a seconddifference signal 2072 derived from sorted difference signal 2062.Sorted first difference signal 2012 exhibits an initially steep slopewhich levels out relatively quickly. In contrast, sorted firstdifference signal 2062 exhibits a relatively steadier slope, whichlevels out relatively later than that of sorted first difference signal2012. Accordingly, second difference signal 2022 exhibits activityearly, which declines relatively quickly, while second difference signal2072 exhibits activity further along, declining relatively later thansecond difference signal 2022. The processing equipment may beconfigured to quantify the behavior of the second difference signal,which may allow the processing equipment to distinguish differences incharacter between, for example, illustrative second difference signals2022 and 2072. Quantification of behavior may be used to classifyphysiological data as having a dicrotic notch or not having a dicroticnotch.

Step 1910 may include processing equipment determining an algorithmsetting based on the second difference signal of step 1908. In someembodiments, portions of the second difference may be compared todetermine a metric. For example, referencing plot 2020 of FIG. 20, theratio (or the difference) of the averages of the second and thirdquartiles of second difference signal 2022 may be determined andcompared to a threshold. The ratio of the averages of the second andthird quartiles of second difference signal 2022 is likely near one andthe difference of the averages of the second and third quartiles ofsecond difference signal 2022 is likely near zero. The processingequipment may compare the ratio to a threshold, compare the differenceto a threshold, or both, to classify the data. In some embodiments, theratio and difference may be combined into a single metric, which may becompared to a threshold. In a further example, referencing plot 2070 ofFIG. 20, the ratio (or the difference) of the averages of the second andthird quartiles of second difference signal 2072 may be determined andcompared to a threshold. The ratio of the averages of the second andthird quartiles of second difference signal 2072 is likely not near one(and relatively further from one than the ratio for second differencesignal 2022) and the difference of the averages of the second and thirdquartiles of second difference signal 2072 is likely nonzero (andrelatively further from zero than the difference for second differencesignal 2022). Accordingly, the processing equipment may detect thepresence of a dicrotic notch (and optionally classify the physiologicaldata) using a metric based on the second difference signal.

FIG. 21 is a flow diagram 2100 of illustrative steps for determining analgorithm setting based on a skewness value of a physiological signal,in accordance with some embodiments of the present disclosure. FIG. 22is a panel showing an illustrative contour plot 2200 of instances ofskewness value and correlation lag value, in accordance with someembodiments of the present disclosure. FIG. 22 will be referred to belowduring the discussion of the illustrative steps of flow diagram 2100.

Step 2102 may include processing equipment receiving physiological datafrom a physiological sensor, memory, any other suitable source, or anycombination thereof. For example, referring to system 300 of FIG. 3, theprocessing equipment may receive a window of physiological data frominput signal generator 310. Sensor 318 of input signal generator 310 maybe coupled to a subject, and may detect physiological activity such as,for example, RED and/or IR light attenuation by tissue, using aphotodetector. In some embodiments, physiological signals generated byinput signal generator 310 may be stored in memory (e.g., RAM 54 of FIG.2, QSM 72 and/or other suitable memory) after being pre-processed bypre-processor 320. In such cases, step 2102 may include recalling datafrom the memory for further processing.

Step 2104 may include processing equipment determining a skewness valuebased on the received physiological data of step 2102. In someembodiments, the processing equipment may use an expression such as Eq.16 to determine the skewness value. In some embodiments, the processingequipment may perform one or more signal conditioning operations priorto determining the skewness value. For example, in some embodiments, theprocessing equipment may subtract the mean value of the physiologicaldata to center the data about zero.

Step 2106 may include processing equipment determining an algorithmsetting based on a reference relationship between the determinedskewness metric and a value indicative of a physiological rate. In someembodiments, the reference relationship may be represented by afunction, a look-up table, a mapping, any other suitable representation,or any combination thereof. For example, the processing equipment maydetermine whether to apply a FIR filter to the physiological data basedon the skew metric. In a further example, the processing equipment maydetermine an amount of filtering to apply to the physiological databased on the skew metric.

FIG. 22 is a panel showing an illustrative contour plot 2200 ofinstances of skewness value and correlation lag value, in accordancewith some embodiments of the present disclosure. The abscissa of plot2200 represents the correlation lag values, and the ordinate representsthe values indicative of skewness in arbitrary units. Region 2202corresponds to relatively higher number instances, while region 2204corresponds to an intermediate number of instances, while the remainingtwo-dimensional space of plot 2200 corresponds to relatively lowernumber of instances. Regions 2202 and 2204 show that as lag valueincreases, the skew values generally become more negative. Therefore,the skew of physiological data may be indicative of the lag value orrate corresponding to the physiological signal. In some embodiments, alook-up table, data structure, or other reference including datarelating a skewness value and a value indicative of correlation lag(e.g., such as that represented by plot 2200) may be stored in memory.For example, in some implementations, the processing equipment maydetermine a skewness value based on a physiological signal, and refer toa look-up table to determine a lag value estimate based on the skewnessvalue. In a further example, a function (e.g., piecewise or continuous)or other relationship may be derived to approximately describe therelationship shown in plot 2200. The skew value or lag value estimatemay be used to classify the physiological data. For example, a skewvalue below a threshold may be indicative of physiological data of anadult or a person who may have a dicrotic notch. As another example, askew value above a threshold may be indicative of physiological data ofa neonate or a person who may not have a dicrotic notch. The skew value,lag value estimate, and/or classification may be used to determine analgorithm setting as described in connection with step 2106 of FIG. 21.

FIG. 23 is a flow diagram 2300 of illustrative steps for determining analgorithm setting based on a skewness value and a difference signal ofthe physiological signal, in accordance with some embodiments of thepresent disclosure. FIG. 24 is a panel showing an illustrative plot 2400of classifier reference data based on a skewness value and a sorteddifference signal, in accordance with some embodiments of the presentdisclosure. FIG. 24 will be referred to below during the discussion ofthe illustrative steps of flow diagram 2300.

Step 2302 may include processing equipment receiving physiological datafrom a physiological sensor, memory, any other suitable source, or anycombination thereof. For example, referring to system 300 of FIG. 3, theprocessing equipment may receive a window of physiological data frominput signal generator 310. Sensor 318 of input signal generator 310 maybe coupled to a subject, and may detect physiological activity such as,for example, RED and/or IR light attenuation by tissue, using aphotodetector. In some embodiments, physiological signals generated byinput signal generator 310 may be stored in memory (e.g., RAM 54 of FIG.2, QSM 72 and/or other suitable memory) after being pre-processed bypre-processor 320. In such cases, step 2302 may include recalling datafrom the memory for further processing.

Step 2304 may include processing equipment generating a differencesignal (e.g., by calculating a sequence of difference values betweenadjacent samples of the physiological data). In some embodiments, theprocessing equipment may perform a subtraction between values ofadjacent samples. In some embodiments, the processing equipment maycalculate the differences by calculating a first derivative of thephysiological data. For example, the processing equipment may computeforward differences, backward differences, or central differencesbetween each pair of adjacent points to generate a difference signal. Ina further example, the processing equipment may compute a numericalderivative at each point in the data, generating a difference signal.Any suitable difference technique may be used by the processingequipment to generate the difference signal.

Step 2306 may include processing equipment sorting the difference valuesof step 2304. The processing equipment may sort the values in ascendingor descending order, either of which causes the negative and positivevalues to be separated. Referencing sorted values in ascending order,the most negative values come first followed by less negative values,positive values, and finally larger positive values. Accordingly thesorted values can be separated into positive values and negative values,and the two sets of values may be processed separately.

Step 2308 may include processing equipment determining at least onemetric based on the sorted difference signal of step 2306. In someembodiments, the processing equipment may determine the at least onemetric using the illustrative techniques described in the context ofFIGS. 11-22, or below in the context of FIGS. 25-41, or a combinationthereof.

Step 2310 may include processing equipment determining a skewness valuebased on the received physiological data of step 2302. In someembodiments, the processing equipment may use an expression such as Eq.16 to determine the skewness value. In some embodiments, the processingequipment may perform one or more signal conditioning operations priorto determining the skewness value. For example, in some embodiments, theprocessing equipment may subtract the mean value of the physiologicaldata to center the data about zero.

Step 2312 may include processing equipment determining an algorithmsetting based on the determined skewness value and the at least onemetric. In some embodiments, the algorithm setting may be based on areference relationship between the skewness value and the at least onemetric that be represented by a function, a look-up table, a mapping, aneural network, any other suitable representation, or any combinationthereof.

FIG. 24 is a panel showing an illustrative plot 2400 of classifierreference data based on a skewness value and two metrics derived from asorted difference signal, in accordance with some embodiments of thepresent disclosure. Plot 2400 shows the classification of physiologicaldata based on a skewness value, a first metric value calculated usingthe illustrative techniques of flow diagram 1300, and a second metricvalue calculated using the illustrative techniques of flow diagram 1500.Region 2402 corresponds to data for neonates, while region 2404corresponds to data exhibiting a dicrotic notch. The relatively cleangrouping shown in plot 2400 may be used to classify physiological data.In some embodiments, the data in plot 2400 may be filtered data toimprove the distinct grouping of the data. Data, such as thatrepresented by plot 2400 may be used as a reference to classifyphysiological data, and accordingly may be stored in suitable memory inany suitable format (e.g., a data table, a set of data tables, afunction, or any other suitable format). In some embodiments, data ofinstances such as that shown in plot 2400 may be inputted into anearest-neighbor probability calculation to determine, for each valuetriple (i.e., each set of three values that can be represented by apoint in the 3-D space of plot 2400), a probability that the databelongs to a classification. The regions of high probability may alignsubstantially with respective regions 2402 and 2404, albeit with somesmoothing from the nearest-neighbor calculation. In some embodiments,

The foregoing techniques for determining an algorithm setting may beused alone or in combination to determine an algorithm setting. FIG. 25is a flow diagram 2500 of illustrative steps for determining analgorithm setting based on a combination of metrics, in accordance withsome embodiments of the present disclosure.

Step 2502 may include processing equipment determining a first metric,using any suitable technique in accordance with the present disclosure.For example, the processing equipment may determine the first metricusing any of the techniques described in the context of FIGS. 11-24, orbelow in the context of FIGS. 26-41. Step 2504 may include processingequipment determining a second metric, using any suitable technique inaccordance with the present disclosure. For example, the processingequipment may determine the second metric using any of the techniquesdescribed in the context of FIGS. 11-24, or below in the context ofFIGS. 26-41. Step 2506 may include processing equipment determining anNth metric, using any suitable technique in accordance with the presentdisclosure. For example, the processing equipment may determine the Nthmetric using any of the techniques described in the context of FIGS.11-24, or below in the context of FIGS. 26-41. In some embodiments, eachmetric of steps 2502-2506 may be of a different type (e.g., determinedusing a different technique). In some embodiments, each metric of steps2502-2506 may be of the same type, although different settings may beused (e.g., determined using the same technique but using differentthresholds, offsets, or other settings).

Step 2508 may include processing equipment determining an algorithmsetting based on the metrics of steps 2502-2506. In some embodiments,two or more of metrics may be combined by, for example, averaging,summing, multiplying, performing any other suitable combinationtechnique, or any combination of techniques thereof. In someembodiments, the processing equipment may select a metric from among themetrics. For example, the processing equipment may select the largestmetric, or the smallest metric. In some embodiments, the processingequipment may exclude a metric from a combination of the metrics. Forexample, the processing equipment may exclude the largest metric, thesmallest metric, or both, when determining a combined metric value suchas, for example, an average, a sum, a product, any other suitablecombined metric value, or any combination thereof.

FIG. 26 is a panel showing an illustrative set of de-trending metricvalues and illustrative contours for an illustrative combination ofde-trending metrics, in accordance with some embodiments of the presentdisclosure. Plot 2600 shows sets of points 2602 and 2604, correspondingto dicrotic notch and neonate data, respectively, derived by generatingcoordinate pairs of two de-trending metric values for multiple datasets. The abscissa and ordinate of plot 2600 ranges from zero to one,indicating normalized de-trending metric values. Plot 2650 shows anillustrative contour plot generated by using a nearest-neighborprobability classifier (e.g., 10-point technique as shown in FIG. 26)based on sets of points 2602 and 2604 of plot 2600. The abscissa andordinate of plot 2650 ranges from zero to one hundred, indicating onehundred times the de-trending metric values of plot 2600 (i.e., a simplemultiplicative scaling). The contour plot includes two regions 2652 and2654, corresponding to a high probability of a dicrotic notch and a highprobability of a neonate, respectively. Note that the region betweenregions 2652 and 2654 corresponds to relatively lower probabilities ofeither a dicrotic notch or neonate signal type.

FIG. 27 is a panel showing three illustrative sets of de-trending metricvalues, taken for different window sizes, in accordance with someembodiments of the present disclosure. The abscissa and ordinate ofplots 2700, 2710, and 2720 both range from zero to one, indicatingnormalized de-trending metric values. Plot 2700 shows sets of points2702 and 2704, corresponding to dicrotic notch and neonate data,respectively, derived by generating coordinate pairs of two de-trendingmetric values for multiple data sets using six-second windows. Plot 2710shows sets of points 2712 and 2714, corresponding to dicrotic notch andneonate data, respectively, derived by generating coordinate pairs oftwo de-trending metric values for multiple data sets usingeighteen-second windows. Plot 2720 shows sets of points 2722 and 2724,corresponding to dicrotic notch and neonate data, respectively, derivedby generating coordinate pairs of two de-trending metric values formultiple data sets using thirty-second windows. As the window sizeincreases from the six-second window, it can be seen that the setspartition more cleanly, indicating that a classification may beperformed more accurately.

In some embodiments, empirical data may be used to set an algorithmsetting. For example, processing equipment may map two metrics to aparticular classification. The mapping may include a look-up table, afunction describing the classification boundary, a nearest neighborclassifier, a neural network, or any other linear or non-linear mapping,or any combination thereof. Referencing FIG. 26, the processingequipment may, for example, fit a function to the boundary betweenregions 2602 and 2604. Note that any suitable combination of anysuitable number of metrics may be mapped to a classification. In someembodiments

FIG. 28 is a flow diagram 2800 of illustrative steps for temporallymonitoring metrics, in accordance with some embodiments of the presentdisclosure.

Step 2802 may include processing equipment receiving physiological dataover time from a physiological sensor, memory, any other suitablesource, or any combination thereof. For example, referring to system 300of FIG. 3, the processing equipment may receive a window ofphysiological data from input signal generator 310. Sensor 318 of inputsignal generator 310 may be coupled to a subject, and may detectphysiological activity such as, for example, RED and/or IR lightattenuation by tissue, using a photodetector. In some embodiments,physiological signals generated by input signal generator 310 may bestored in memory (e.g., RAM 54 of FIG. 2, QSM 72 and/or other suitablememory) after being pre-processed by pre-processor 320. In such cases,step 2802 may include recalling data from the memory for furtherprocessing.

Step 2804 may include processing equipment determining a sequence ofmetric values over time. In some embodiments, for example, theprocessing equipment may determine a metric value or a noise metricvalue every second based on the most recent window of physiologicaldata.

Step 2806 may include processing equipment determining a temporal changein the sequence of metric values of step 2804. In some embodiments, theprocessing equipment may determine a difference between consecutivemetric values, and if the difference exceeds a threshold, the processingequipment may determine that a temporal change has occurred. In someembodiments, the processing equipment may determine a difference betweeneach metric value and a reference value (e.g., an initial metric value),and if the difference exceeds a threshold, the processing equipment maydetermine that a temporal change has occurred. In some embodiments, theprocessing equipment may determine that a temporal change has occurredwhen the difference exceeds a threshold for a certain amount of time.

Step 2808 may include processing equipment adjusting one or morealgorithm settings based on the metric values. For example, if atemporal change exceeds a threshold, the processing equipment mayperform any of the techniques described in the context of FIGS. 11-28,or below in the context of FIGS. 30-41 to classify the physiologicalsignal again.

In some embodiments, the processing equipment may determine one or morealgorithm settings, and adjust the one or more algorithm settings inresponse to one or more metric values. For example, the processingequipment may receive physiological data, and determine a metric valueindicative of a physiological classification. The physiologicalclassification may be based on the presence of a dicrotic notch (e.g.,dicrotic notch, no dicrotic notch, neonate), physiological rate (e.g.,large or small heart rate), pulse shape, any physiologicalclassification, or any combination thereof. The processing equipment maydetermine an algorithm setting based on the physiological classificationsuch as, for example, a mode, a qualification test, a qualificationcriterion, a threshold value, a signal conditioning setting, any othersuitable algorithm setting, or any combination thereof. In someinstances, subsequent to determining the algorithm setting, theprocessing equipment may determine a second metric value indicative of adifferent physiological classification than determined previously basedon subsequent physiological data. The second metric value may be thesame metric above having an updated value, or a different metric, whichindicates the different physiological classification. The processingequipment may then determine an algorithm setting based on the differentphysiological classification such as, for example, a mode, aqualification test, a qualification criterion, a threshold value, asignal conditioning setting, any other suitable algorithm settingdifferent from the previous algorithm setting, or any combinationthereof. Accordingly, the processing equipment may update algorithmsettings as changes occur in the physiological data, in the state of therate algorithm, or both.

In some embodiments, one or more metrics indicative of noise (e.g., anoise metric) in the physiological data may be used to determine one ormore algorithm settings. A noise metric may provide an indication of therelative noise level, absolute noise level, type of noise, any othernoise property, or any combination thereof. For example, physiologicaldata corresponding to a physiological rate may be expected to exhibitsubstantially oscillatory behavior. Differences, such as a firstderivative, in the physiological data may also be expected to exhibitoscillatory behavior and occur in an expected range. FIG. 29 is a panelshowing illustrative PPG signals with and without a dicrotic notch,corresponding difference signals for each, and corresponding sorteddifference signals for each in accordance with some embodiments of thepresent disclosure. The abscissas of the plots of FIG. 29 are in unitsof sample number, while the ordinates are shown in arbitrary units. Plot2900 shows PPG signal 2902 (indicative of transmitted intensity) havinga dicrotic notch. Plot 2910 shows difference signal 2912 derived bycalculating differences (e.g., forward difference, backward differences,central difference, derivatives, or any other suitable difference) ateach point of PPG signal 2902. Plot 2920 shows sorted difference signal2922, generated by sorting the values of difference signal 2912 inascending order by value. Plot 2950 shows PPG signal 2952 (indicative oftransmitted intensity), which does not exhibit a dicrotic notch. Plot2960 shows difference signal 2962 derived by calculating differences ateach point of PPG signal 2952. Plot 2970 shows sorted difference signal2972, generated by sorting the values of difference signal 2262 inascending order by value. Sorted difference signals 2922 and 2972exhibit different shapes, which may be taken into account by theprocessing equipment when determining a noise metric. The illustrativetechniques discussed in the context of FIGS. 30, 32, 36, 37, 39 and 41may applied to any suitable physiological data such as, for example,those shown in FIG. 29 to classify the data, set an algorithm setting,or both.

FIG. 30 is a flow diagram 3000 of illustrative steps for determining anoise metric from a line fit of a sorted difference signal, inaccordance with some embodiments of the present disclosure. FIG. 31 is apanel showing illustrative PPG signals with and without a dicroticnotch, corresponding difference signals for each, corresponding sorteddifference signals for each, and corresponding line fits for each, inaccordance with some embodiments of the present disclosure. FIG. 31 willbe referred to below during the discussion of the illustrative steps offlow diagram 3000.

Step 3002 may include processing equipment receiving physiological datafrom a physiological sensor, memory, any other suitable source, or anycombination thereof. For example, referring to system 300 of FIG. 3, theprocessing equipment may receive a window of physiological data frominput signal generator 310. Sensor 318 of input signal generator 310 maybe coupled to a subject, and may detect physiological activity such as,for example, RED and/or IR light attenuation by tissue, using aphotodetector. In some embodiments, physiological signals generated byinput signal generator 310 may be stored in memory (e.g., RAM 54 of FIG.2, QSM 72 and/or other suitable memory) after being pre-processed bypre-processor 320. In such cases, step 3002 may include recalling datafrom the memory for further processing.

Step 3004 may include processing equipment generating a differencesignal (e.g., by calculating a sequence of difference values betweenadjacent samples of the physiological data). In some embodiments, theprocessing equipment may perform a subtraction between values ofadjacent samples. In some embodiments, the processing equipment maycalculate the differences by calculating a first derivative of thephysiological data. For example, the processing equipment may computeforward differences, backward differences, or central differencesbetween each pair of adjacent points to generate a difference signal. Ina further example, the processing equipment may compute a numericalderivative at each point in the data, generating a difference signal.Any suitable difference technique may be used by the processingequipment to generate the difference signal.

Step 3006 may include processing equipment sorting the difference valuesof step 3004. The processing equipment may sort the values in ascendingor descending order, either of which causes the negative and positivevalues to be separated. Referencing sorted values in ascending order,the most negative values come first followed by less negative values,positive values, and finally larger positive values.

Step 3008 may include processing equipment fitting a line to at least aportion of the sorted difference signal of step 3006. In someembodiments, the processing equipment may perform a linear regression(e.g., a least-squares regression or a weighted least-squaresregression) using at least a portion of the sorted difference signal. Insome embodiments, the processing equipment may fit a line using everypoint of the sorted difference signal. In some embodiments, theprocessing equipment may fit a line using a portion of the sorteddifference signal. For example, the processing equipment may omit one ormore points at one or both ends of the sorted difference signal whendetermine the line fit. In some embodiments, the line fit may include aslope value and an ordinate intercept value (e.g., using a y=mx+b linearform where m is the slope and b is the intercept). In some embodiments,the processing equipment may determine if a dicrotic notch is present(e.g., based on a de-trending metric), and determine line fits on eitherside of the knee in the sorted difference signal. For example, plot 2920of FIG. 29 shows sorted difference signal 3122 (e.g., derived from a PPGsignal having a dicrotic notch), exhibiting a knee at an abscissa valueof about 50.

Step 3010 may include processing equipment calculating upper and lowerthresholds for the line fit of step 3008. In some embodiments, the upperand lower thresholds may be lines parallel to the line fit (i.e., havingthe same slope) with the vertical-intercept (or other reference point)at a fixed difference from the line fit (e.g., the thresholds may begiven by y=mx+b±C where C is the fixed difference). In some embodiments,the upper and lower thresholds may be lines with slopes other than theslope of the line fit (e.g., the thresholds may be given by y=nx+b₁ andy=px+b₂ where n and p are the upper and lower threshold slopes). Theupper and lower thresholds may be generated by the processing equipmentusing any suitable function.

Step 3012 may include processing equipment identifying points of thesorted difference signal that are outside of the thresholds calculatedat step 3010. Plot 3100 of FIG. 31 shows PPG signal 3102 (indicative oftransmitted intensity) having a dicrotic notch. Plot 3110 showsdifference signal 3112 derived by calculating differences (e.g., forwarddifference, backward differences, central difference, derivatives, orany other suitable difference) at each point of PPG signal 3102. Plot3120 shows sorted difference signal 3122, generated by sorting thevalues of difference signal 3112 in ascending order by value. Plot 3150shows PPG signal 3152 (from a neonate), which does not exhibit adicrotic notch. Plot 3160 shows difference signal 3162 derived bycalculating differences at each point of PPG signal 3152. Plot 3170shows sorted difference signal 3172, generated by sorting the values ofdifference signal 3162 in ascending order by value. The dashed lines inplots 3120 and 3170 represent line fits (a piecewise line fit in plot3120), while the dashed-dotted lines represent upper and lowerthresholds for each line fit.

Step 3014 may include processing equipment determining a noise metricbased on the identified points of step 3012. In some embodiments, theprocessing equipment may calculate the ratio of points identified atstep 3012 to the total number of points of the sorted difference signal.For example, the noise metric may be set equal to the ratio of pointsidentified at step 3012 to the total number of points (e.g., relativelylow numbers of noise points result in a noise metric relatively nearerto zero).

Referencing FIG. 31, PPG signal 3102 and corresponding difference signal3112 exhibit relatively low noise in the signal, with the pulses beingrelatively consistently spaced and having a relatively consistent shape.Accordingly, as expected, no noise points are identified. PPG signal3152 and corresponding difference signal 3162 exhibit distortion basedon the baseline shifts and the varying shape of the pulses. Accordingly,as expected, noise points are identified at the ends of sorteddifference signal 3172. Typically a low noise PPG signal will have arelatively smooth corresponding sorted difference signal because thedifferences are expected to fall within a certain distribution. Withincreasing levels of noise, the number of extreme (e.g., outside of thecertain distribution) positive difference values and extreme negativedifference values increases. As a result, the negative end of the sorteddifference signal will typically begin to significantly bend downwardsand the positive end of the sorted difference signal will start to bendupwards. This effect of noise can be seen in plot 3170, particularly atthe positive end.

FIG. 32 is a flow diagram 3200 of illustrative steps for determining anoise metric from a segmented line fit of a sorted difference signal, inaccordance with some embodiments of the present disclosure. FIG. 33 is apanel showing an illustrative difference signal derived from a PPGsignal, a sorted difference signal, and corresponding segmented linefits, in accordance with some embodiments of the present disclosure.Additionally, FIG. 34 is a partial view of the sorted difference signalof FIG. 33, taken from circle 3400, showing portions of two groups, inaccordance with some embodiments of the present disclosure. FIGS. 33-35will be referred to below during the discussion of the illustrativesteps of flow diagram 3200.

Step 3202 may include processing equipment receiving physiological datafrom a physiological sensor, memory, any other suitable source, or anycombination thereof. For example, referring to system 300 of FIG. 3, theprocessing equipment may receive a window of physiological data frominput signal generator 310. Sensor 318 of input signal generator 310 maybe coupled to a subject, and may detect physiological activity such as,for example, RED and/or IR light attenuation by tissue, using aphotodetector. In some embodiments, physiological signals generated byinput signal generator 310 may be stored in memory (e.g., RAM 54 of FIG.2, QSM 72 and/or other suitable memory) after being pre-processed bypre-processor 320. In such cases, step 3202 may include recalling datafrom the memory for further processing.

Step 3204 may include processing equipment generating a differencesignal (e.g., by calculating a sequence of difference values betweenadjacent samples of the physiological data). In some embodiments, theprocessing equipment may perform a subtraction between values ofadjacent samples. In some embodiments, the processing equipment maycalculate the differences by calculating a first derivative of thephysiological data. For example, the processing equipment may computeforward differences, backward differences, or central differencesbetween each pair of adjacent points to generate a difference signal. Ina further example, the processing equipment may compute a numericalderivative at each point in the data, generating a difference signal.Any suitable difference technique may be used by the processingequipment to generate the difference signal. As an illustrative example,plot 3300 of FIG. 33 shows difference signal 3302, which is a calculatedfirst derivative signal of a PPG signal.

Step 3206 may include processing equipment sorting the values of thedifference signal of step 3204. The processing equipment may sort thevalues in ascending or descending order. Referencing sorted values inascending order, the most negative values come first followed by lessnegative values, positive values, and finally larger positive values.

Step 3208 may include processing equipment dividing the sorteddifference signal of step 3206 into N segments. In some embodiments, theN segments may be of equal length (e.g., each may include the samenumber of samples). In some embodiments, the N segments may havedifferent lengths. The value N may be any suitable positive integer,greater than or equal to one. In some embodiments, step 3208 may includethe processing equipment determining a beginning and ending data pointnumber corresponding to each segment, a length of each segment (e.g.,number of data points), any other suitable metric for dividing a signalinto one or more segments, or any combination thereof. In someembodiments, the processing equipment may generate N new signals, eachcorresponding to one of the N segments.

Step 3210 may include processing equipment determining the average slopeof each segment of the N segments of step 3208. In some embodiments, theprocessing equipment may perform a linear regression (e.g., aleast-squares regression or a weighted least-squares regression) foreach segment to determine the slope. In some embodiments, the processingequipment may determine the average slope of a segment as the slope ofthe line coincident with the endpoints of that segment. The processingequipment may use any suitable technique to determine an average slopeof each segment.

Step 3212 may include processing equipment grouping adjacent segmentswith similar slopes. Based on the average slopes of each segment, asdetermined at step 3210, the processing equipment may group segmentsinto collective, larger segments if the slopes are sufficiently similar.In some embodiments, the slopes of each adjacent pair of segments may becompared, and if the slopes are sufficiently similar, the segments maybe grouped. For example, the ratio of the slopes of each adjacent pairof segments may be determined, and if the ratio is between 0.5 and 2then the segments may be grouped. Any suitable range of ratios may beused to group segments, in accordance with the present disclosure. Theresulting number of groups may be less than or equal to the initialnumber of segments N. As an illustrative example, plot 3350 of FIG. 33shows sorted difference signal 3352, which includes sorted data pointsof difference signal 3302. Sorted difference signal 3352 isillustratively grouped into four groups, G1, G2, G3 and G4, as shown bythe vertical dotted lines in FIG. 33. The ends of the sorted differencesignal are referred to herein as terminal ends.

Step 3214 may include processing equipment determining upper and lowerthresholds of the first and last groups of steps 3212. The resulting oneor more groups of step 3212 may include a first group (e.g., theleftmost group, using the abscissa of plot 3310 of FIG. 33 as areference) and a last group (e.g., the rightmost group, using theabscissa of plot 3310 of FIG. 33 as a reference). The first group may beassociated with relatively more negative, or smaller, values of thesorted difference signal. The last group may be associated withrelatively more positive, or larger, values of the sorted differencesignal. As an illustrative example, plot 3350 of FIG. 33 shows upper andlower thresholds for the four groups, G1, G2, G3 and G4 by each pair ofdashed-dotted lines corresponding to each group. The partial view ofFIG. 34, taken from circle 3400 in FIG. 33, shows a magnified view ofportions of groups G1 and G2. The line including the average slope isshown by dashed line 3354. The upper and lower thresholds are shown bydashed-dotted lines 3356 and 3358, respectively. In some embodiments,the upper and lower thresholds of the first and last groups may extendto respective terminal ends of the sorted difference signal. Relative tothe line fit, the threshold may be offset a fixed amount, offset afunction of the slope of the line fit, offset a function of the goodnessof the line fit, or a combination thereof.

Step 3216 may include processing equipment identifying noise points ofat least one end of the sorted difference signal of step 3206. In someembodiments, the processing equipment may identify noise points in thesorted difference signal as lying outside of the upper and lowerthresholds of step 3214. For example, steep changes in slope may cause aportion of the sorted difference signal to lie outside a set ofthresholds for a group. Points identified as lying outside of thethresholds may be identified as noise points. In some embodiments,points lying outside of the upper and lower thresholds, and all pointsfrom the first excursion to the terminal end of the group may beidentified as noise, regardless of whether some or all lie within thethresholds. In some embodiments, the processing equipment may start atthe interior end of the first and last groups, away from the terminalends, and progress outward towards to the terminal ends. For examplereferencing group G1 of FIG. 33, the processing equipment may begin atthe point at or near the junction of groups G1 and G2, and progressleftward to the end of the sorted difference signal.

As an illustrative example, sorted difference signal 3352 of plot 3350of FIG. 33 lies within the upper and lower thresholds for all of thefour groups, G1, G2, G3 and G4. Accordingly, the processing equipmentwould not necessarily identify any noise points in this circumstance. Asa further illustrative example, FIG. 35 is a plot of a portion of afirst group of an illustrative sorted difference signal 3502, andcorresponding thresholds, in accordance with some embodiments of thepresent disclosure. Sorted difference signal 3502 is shown to crosslower threshold 3508 at point 3520. Accordingly, in this circumstance,the processing equipment may identify noise points as all points between(and possibly including) point 3520 and the group endpoint 3530 at theterminal end of the first group.

In some embodiments, the segments do not extend to the end of the sorteddifference signal, as fitting the lines to noise points may beundesirable. The thresholds associated with the first and last groupsmay, however, extend to the end of the sorted difference signal tolocate noise points at each respective end. In some embodiments, theprocessing equipment need not determine both maximum and minimumthresholds for each line. For example, the processing equipment may onlycalculate a minimum threshold for the negative end of the sorteddifference signal and a maximum threshold for the positive end, becausenoise points would be expected to diverge accordingly at the ends ratherthan flatten out.

Step 3218 may include processing equipment determining a noise metricbased on the identified noise points of step 3216. In some embodiments,the ratio of the number of noise points to total points may bedetermined (e.g., using the illustrative techniques of flow diagram3000). For example, the noise metric may be set equal to the ratio ofnoise points to total points (e.g., relatively low numbers of noisepoints result in a noise metric relatively nearer to zero). In someembodiments, a noise metric may be determined at step 3218 based on asignal-to-noise type comparison. For example, FIG. 36 is a flow diagram3600 of illustrative steps for determining a noise metric based onidentified noise points, using a signal-to-noise type comparison, inaccordance with some embodiments of the present disclosure. Theprocessing equipment may determine the noise metric based on the noisepoints identified at step 3216, if any. Step 3602 may includedetermining whether no noise points were identified, in which case theprocessing equipment may set the noise metric equal to zero at step3604. Step 3606 may include determining whether both positive andnegative noise points were identified, in which case at step 3608 theprocessing equipment may determine the noise metric M based on Eq. 19:

$\begin{matrix}{M = {1 - \sqrt{\frac{S}{N}}}} & (19)\end{matrix}$in which S is the difference between the positive and negative signalamplitudes, and N is equal to two times the maximum of the last signalvalue and the first signal value of the sorted difference signal. Step3610 may include determining whether only a positive noise point wasidentified, in which case at step 3612 the processing equipment maydetermine the noise metric M based on Eq. 19 in which S is the positivesignal amplitude, and N is equal to two times the maximum of the lastvalue of the sorted difference signal. Step 3614 may include determiningwhether only a negative noise point was identified, in which case atstep 3616 the processing equipment may determine the noise metric Mbased on Eq. 19 in which S is the negative signal amplitude, and N isequal to two times the maximum of the first value of the sorteddifference signal. Flow diagram 3600 is merely illustrative, and theprocessing equipment may determine any suitable metric indicative ofnoise. For example, the processing equipment may determine thedifference between the maximum and minimum values of the sorteddifference signal as a metric. In further example, the processingequipment may determine the difference between the positive and negativesignal amplitudes as a metric.

FIG. 37 is a flow diagram 3700 of illustrative steps for determining anoise metric based on two portions of physiological data, in accordancewith some embodiments of the present disclosure. FIG. 38 is a panelshowing an illustrative PPG signal, a difference signal derived from thePPG signal, and corresponding sorted difference signals, in accordancewith some embodiments of the present disclosure. FIG. 38 will bereferred to below during the discussion of the illustrative steps offlow diagram 3700. The analysis of two portions of the physiologicalsignal may aid in identifying noise, quantifying noise, determining anonset of or reduction in noise, or a combination thereof. For example,when noise is low, adjacent portions of physiological data havingsufficient size (e.g., a multiple of the period, or otherwise large sizecompared to the period of a physiological rate) should have similarsorted difference signals. As noise appears the buffer, the shape anddistribution of points in the sorted difference signal containing thenoise are expected to be different than the portion that does notcontain the noise. Further, if both portions include noise, consistencybetween the two sorted difference signals is not necessarily expected.Accordingly, the illustrative techniques of flow diagram 3700 mayprovide a convenient noise metric especially with intermittent noise andidentifying when noise begins.

Step 3702 may include processing equipment receiving physiological datafrom a physiological sensor, memory, any other suitable source, or anycombination thereof. For example, referring to system 300 of FIG. 3, theprocessing equipment may receive a window of physiological data frominput signal generator 310. Sensor 318 of input signal generator 310 maybe coupled to a subject, and may detect physiological activity such as,for example, RED and/or IR light attenuation by tissue, using aphotodetector. In some embodiments, physiological signals generated byinput signal generator 310 may be stored in memory (e.g., RAM 54 of FIG.2, QSM 72 and/or other suitable memory) after being pre-processed bypre-processor 320. In such cases, step 3702 may include recalling datafrom the memory for further processing.

Step 3704 may include processing equipment generating a first differencesignal based on a first portion of the physiological data of step 3702(e.g., by calculating a sequence of difference values between adjacentsamples of the physiological data). In some embodiments, the processingequipment may perform a subtraction between values of adjacent samples.In some embodiments, the processing equipment may calculate thedifferences by calculating a first derivative of the physiological data.For example, the processing equipment may compute forward differences,backward differences, or central differences between each pair ofadjacent points of a portion of the physiological data to generate afirst difference signal. In a further example, the processing equipmentmay compute a numerical derivative at each point of a portion of thephysiological data, generating a first difference signal. Any suitabledifference technique may be used by the processing equipment to generatethe first difference signal.

Step 3706 may include processing equipment sorting the values of thefirst difference signal of step 3704. The processing equipment may sortthe values of the first difference signal in ascending or descendingorder. Referencing sorted values in ascending order, the most negativevalues come first followed by less negative values, positive values, andfinally larger positive values.

Step 3708 may include processing equipment determining a line fit of thesorted first difference signal of step 3706. In some embodiments, theprocessing equipment may perform a linear regression (e.g., aleast-squares regression or a weighted least-squares regression) usingat least a portion of the sorted first difference signal. In someembodiments, the processing equipment may fit a line using every pointof the sorted difference signal. In some embodiments, the processingequipment may fit a line using a portion of the sorted differencesignal. For example, the processing equipment may omit one or morepoints at one or both ends of the sorted difference signal whendetermining the line fit. In some embodiments, the line fit may includea slope value and an ordinate intercept value (e.g., using the y=mx+blinear form where m is the slope and b is the intercept).

Similar to performing steps 3704-3708, the processing equipment mayperform steps 3710-3714 using a second portion of physiological data.Step 3710 may include processing equipment generating a seconddifference signal based on a second portion of the physiological data ofstep 3702. Step 3712 may include processing equipment sorting the valuesof the second difference signal. Step 3714 may include processingequipment determining a line fit of the sorted second difference signal.The first and second portions of the physiological data may be, but neednot be, exclusive of each other. The physiological data may bepartitioned into two portions using any suitable technique. In someembodiments, the first and second portions may be of equal length (e.g.,each may include the same number of samples or time interval). In someembodiments, the first and second portions may have different lengths(e.g., each may include the same number of samples or time interval).

In an illustrative example, six seconds of data, captured at a samplingrate of 57 Hz, may be received (i.e., about 342 samples). The first 171samples (i.e., samples 1-171) may be included in the first portion, andthe second 171 samples (i.e., samples 172-342) may be included in thesecond portion. Plot 3800 of FIG. 38 shows PPG signal 3802 having about342 samples, while plot 3810 of FIG. 38 shows difference signal 3812,which is a calculated first derivative signal of PPG signal 3802. Firstand second portions are denoted by “P1” and “P2” in FIG. 38,corresponding to first and second difference signals, respectively, eachhaving about 171 samples. In some embodiments, the processing equipmentmay determine the first and second difference signals as portions of asingle difference signal, as shown in plot 3810 where the first portion“P1” of difference signal 3812 may be considered the first differencesignal, and the second portion “P2” of difference signal 3812 may beconsidered the second difference signal. Alternatively, PPG signal 3802could be partitioned into portions and two separate correspondingdifference signals could be calculated and sorted. Plot 3820 of FIG. 38shows sorted first difference signal 3822 and sorted second differencesignal 3832. Although shown as having offset sample numbers forillustration (e.g., allowing slopes of the line fits to be directlycompared but not necessarily the intercepts), the sorted differencesignals 3822 and 3832 could each be numbered about 1-171 samples (e.g.,allowing both the slopes and intercepts of the line fits to becompared). Sorted first difference signal 3822 includes the sorted datapoints of first portion “P1” of difference signal 3812, while sortedsecond difference signal 3832 includes the sorted data points of secondportion “P2” of difference signal 3812. Note that difference signal 3812may have a different length than PPG signal 3802 due to the differencecalculation technique. For example, using a forward difference, adifference signal may include one less point than the correspondingphysiological data from which it was derived.

Step 3716 may include processing equipment determining a differencebetween slopes of the line fits determined at steps 3708 and 3714. Theline fits may be expected to provide similar slopes if the physiologicaldata does not include relatively large amounts of noise. Differences inthe slopes of the line fits may indicate that either or both of thefirst and second portions of the physiological data include appreciablenoise. In some embodiments, the difference in slopes may includecalculating a difference between the slopes, a normalized differencebetween the slopes, a bounded difference of the slopes, a ratio of theslopes, any other suitable comparison metric, or any combinationthereof. In some embodiments, the use of portions of equal length mayallow a direct comparison of slopes of corresponding line fits, becauseeach sorted difference signal will have equivalent domain lengths alongan abscissa, as well as an equivalent range of expected differencevalues. Plot 3820 of FIG. 38 shows line fit 3824 corresponding to sortedfirst difference signal 3822, and line fit 3834 corresponding to sortedfirst difference signal 3832, with each line fit generated using a leastsquares regression. In the illustrated example, the calculated slopes,in arbitrary units, for line fit 3824 and 3834 are 0.2048 and 0.1952,respectively. Using these illustrative numbers, the processing equipmentmay, for example, determine a normalized ratio of 0.95 (i.e., minimumdivided by maximum), a percent difference of 5% (i.e., normalizing bythe average value), a difference of 0.0096 (i.e., subtracting minimumfrom maximum), along with any other suitable comparison metric.

Step 3718 may include processing equipment determining a goodness offit, a noise metric, or both, for the first and second sorted differencesignals. In some embodiments, the processing equipment may compare acomparison metric from step 3816 with one or more thresholds todetermine the goodness of fit, a noise metric, or both. For example, theprocessing equipment may compare the normalized ratio of the slopes witha threshold such as 0.8, and if the ratio is between 0.8 and 1.0, thenthe fit is considered good. In some embodiments, the comparison metricitself, or calculation derived thereof, may be used as a goodness of fitvalue. For example, any or all of Eqs. 20-22 may be used to determinethe goodness of fit and/or a noise metric:

$\begin{matrix}{{{Noise}\mspace{14mu}{Metric}} = {1 - \frac{{Slope}_{\min}}{{Slope}_{\max}}}} & (20) \\{{{Noise}\mspace{14mu}{Metric}} = {{Slope}_{\max} - {Slope}_{\min}}} & (21) \\{{{Noise}\mspace{14mu}{Metric}} = \frac{{Slope}_{\max} - {Slope}_{\min}}{{Slope}_{avg}}} & (22)\end{matrix}$where Slope_(min) is the less of the slope values, Slope_(max) is thegreater of the slope values, and Slope_(Avg) is the average of the slopevalues.

In some embodiments, the processing equipment may perform step 3718without performing steps 3708, 3714, and 3716. For example, theprocessing equipment need not fit a line to the either of the differencesignals to determine a noise metric based on the first and secondportions. In a further example, referencing portions of equal length,the sorted values of the first and second portions may be plottedagainst each other (e.g., if plotted, the values of the first portionmay correspond to the abscissa and the values of the second portion maycorrespond to the ordinate), and a correlation coefficient may bedetermined. The set of points need not be plotted, and may be generatedusing Eq. 23, as shown below:P _(i)=(X _(1,i) ,X _(2,i))  (23)in which P_(i) is the point for index i, and X_(1,i) and X_(2,i) are thevalues of the first and second portions, respectively. In a furtherexample, a set of points may be generated using Eq. 23, and thenormalized difference between the generated set of points and a set ofpoints having the same abscissa value and ordinate value (e.g., ifplotted, the points would lie on a line through the origin having aslope of one).

In some embodiments, the processing equipment may apply any suitablestatistical technique to the two sorted difference signals. For example,the processing equipment may apply a KS Test to the first and secondportions by comparing the sorted difference signals to a predetermineddistribution. In a further example, the processing equipment may use afunction other than a line as a fitting reference. For example, theprocessing equipment may fit a polynomial of any order to the first andsecond difference signals, or any other suitable function, and comparethe fitted functions to each other or to a reference function.

In some embodiments, the illustrative techniques of flow diagram 3700may be applied to more than two portions of physiological data. Forexample, a window of physiological data may be partitioned into threeportions, and three difference signals may be determined and sorted toyield three line fits which may be compared. Any suitable number ofportions may be used to determine a goodness of fit, noise metric, orboth, in accordance with the illustrative techniques of flow diagram3700.

FIG. 39 is a flow diagram 3900 of illustrative steps for estimatingsignal-to-noise ratio based on a sorted difference signal, in accordancewith some embodiments of the present disclosure.

Step 3902 may include processing equipment receiving physiological datafrom a physiological sensor, memory, any other suitable source, or anycombination thereof. For example, referring to system 300 of FIG. 3, theprocessing equipment may receive a window of physiological data frominput signal generator 310. Sensor 318 of input signal generator 310 maybe coupled to a subject, and may detect physiological activity such as,for example, RED and/or IR light attenuation by tissue, using aphotodetector. In some embodiments, physiological signals generated byinput signal generator 310 may be stored in memory (e.g., RAM 54 of FIG.2, QSM 72 and/or other suitable memory) after being pre-processed bypre-processor 320. In such cases, step 3902 may include recalling datafrom the memory for further processing.

Step 3904 may include processing equipment generating a differencesignal (e.g., by calculating a sequence of difference values betweenadjacent samples of the physiological data). In some embodiments, theprocessing equipment may perform a subtraction between values ofadjacent samples. In some embodiments, the processing equipment maycalculate the differences by calculating a first derivative of thephysiological data. For example, the processing equipment may computeforward differences, backward differences, or central differencesbetween each pair of adjacent points to generate a difference signal. Ina further example, the processing equipment may compute a numericalderivative at each point in the data, generating a difference signal.Any suitable difference technique may be used by the processingequipment to generate the difference signal.

Step 3906 may include processing equipment sorting the difference valuesof step 3904. The processing equipment may sort the values in ascendingor descending order. Referencing sorted values in ascending order, themost negative values come first followed by less negative values,positive values, and finally larger positive values.

Step 3908 may include processing equipment determining at least twovalues indicative of noise (e.g., a noise metric) using any of thetechniques described in the context of FIGS. 30-41, along with anymetrics determined using the techniques described in the context ofFIGS. 11-28, or any combination thereof. For example, the processingequipment may determine noise metric values using the techniquesdescribed in the context of FIGS. 30-41, along with any metricsdetermined using the techniques described in the context of FIGS. 11-28,and then select the maximum noise metric value (e.g., with higher noisemetric values corresponding to noisier physiological data) using Eq. 24:Noise Metric=MAX(V ₁ ,V ₂ ,V ₃)  (24)in which V₁, V₂, and V₃ are the noise values from the three techniques.In some embodiments, the processing equipment may select a single value,generate a combined value using a suitable technique (e.g., an average,a weighted average, a product, or some other combination), determine anoise metric based on a lookup table using one or more noise metrics asan input, perform any other suitable calculation of a noise metric, orany combination thereof.

Step 3910 may include processing equipment determining a signal-to-noiseratio estimate based on the values indicative of noise, or a metricderived thereof, from step 3908. In some embodiments, the referencerelationship between the signal-to-noise ratio and the values indicativeof noise may be represented by a function, a look-up table, a mapping,any other suitable representation, or any combination thereof. In someembodiments, the reference relationship may be stored in memory, andaccessed by the processing equipment.

FIG. 40 is a panel showing an illustrative contour plot 4000 ofinstances of signal-to-noise ratio values and ordered statistic noisemetric values, in accordance with some embodiments of the presentdisclosure. The abscissa of plot 4000 represents the noise metricvalues, and the ordinate represents the values indicative ofsignal-to-noise ratio in units of decibels (dB). Region 4002 correspondsto relatively higher number instances, while region 4004 corresponds toan intermediate number of instances, while the remaining two-dimensionalspace of plot 4000 corresponds to relatively lower number of instances.In some embodiments, a look-up table, data structure, or other referenceincluding data relating a noise metric and a value indicative ofsignal-to-noise ratio (e.g., such as that represented by plot 4000) maybe stored in memory. For example, in some implementations, theprocessing equipment may determine a noise metric value based onphysiological signal, and refer to a look-up table to determine asignal-to-noise estimate based on the noise metric value. In a furtherexample, a function (e.g., piecewise or continuous) or otherrelationship may be derived to approximately describe the relationshipshown in plot 4000.

FIG. 41 is a flow diagram 4100 of illustrative steps for determining aresultant noise metric based on a combination of noise metrics, inaccordance with some embodiments of the present disclosure.

Step 4102 may include processing equipment determining a first noisemetric, using any suitable technique in accordance with the presentdisclosure. For example, the processing equipment determine the firstnoise metric using any of the techniques described in the context ofFIGS. 30-41, along with any metrics determined using the techniquesdescribed in the context of FIGS. 11-28. Step 4104 may includeprocessing equipment determining a second noise metric, using anysuitable technique in accordance with the present disclosure. Forexample, the processing equipment may determine the second noise metricusing any of the techniques described in the context of FIGS. 30-41,along with any metrics determined using the techniques described in thecontext of FIGS. 11-28. Step 4106 may include processing equipmentdetermining an Nth noise metric, using any suitable technique inaccordance with the present disclosure. For example, the processingequipment may determine the N^(th) noise metric, where N can be 2 orgreater, using any of the techniques described in the context of FIGS.30-41, along with any metrics determined using the techniques describedin the context of FIGS. 11-28. In some embodiments, each metric of steps4102-4106 may be of a different type (e.g., determined using a differenttechnique). In some embodiments, each noise metric of steps 4102-4106may be of the same type, although different settings may be used (e.g.,determined using the same technique but using different thresholds,offsets, or other settings).

Step 4108 may include processing equipment determining an algorithmsetting based on the noise metrics of steps 4102-4106. In someembodiments, the processing equipment may combine the noise metrics intoa single noise metric. For example, the processing equipment may sum,average, multiply, divide, subtract, or otherwise condense the noisemetrics to determine a resulting metric value indicative of noise. Insome embodiments, the processing equipment may use the noise metrics aan input to a look-up table, reference function, or other reference todetermine a resultant noise metric. For example, the processingequipment may access a N-dimensional look-up table, with each of the Ndimensions corresponding to values of a particular noise metric (e.g., a3-D look-up table indexed by values of three metrics). Such a table maybe generated, for example, from historical data or an analytical model.In some embodiments, the processing equipment may consider multiplemetrics of steps 4102-4106 accordingly to conditional logic. Forexample, the processing equipment may increase the amount of signalprocessing if the value of a particular noise metric exceeds athreshold, even if another metric suggests that the signal contains adicrotic notch the value of a particular noise metric.

In some embodiments, signal conditioning may be applied to aphysiological signal to aid in processing the signal for rateinformation. Signal conditioning may include filtering, de-trending,smoothing, normalizing, derivative limiting, any other suitableconditioning, or any combination thereof. Physiological pulse rates maygenerally fall into a particular range (e.g., 20-300 BPM for humans),and accordingly signal conditioning may be used to reduce the presenceor effects of signal components outside of this particular range.Further, a narrower pulse range may be expected for a subject, based onprevious data for example, and a signal may be conditioned accordingly.The presence of noise may also be addressed using signal conditioning.For example, ambient radiation (e.g., from artificial lighting,monitors, or sunlight) may impart a noise component in a physiologicalsignal. In a further example, electronic noise (e.g., system noise) mayalso impart a noise component in a physiological signal. In a furtherexample, motion or other subject activity may alter a physiologicalsignal, possibly obscuring signal components of interest for extractingrate information. In some embodiments, the Signal Conditioningtechniques discussed herein may apply to operation in any Mode, or whileperforming any other suitable task that may be performed by theprocessing equipment that may involve a physiological signal. Any of thesignal conditioned technique disclosed herein, alone or in anycombination, may be applied, for example, in the context of step 412 offlow diagram 400 of FIG. 4.

FIG. 42 is a flow diagram 4200 of illustrative steps for modifyingphysiological data (e.g., a segment of an intensity signal) using anenvelope, in accordance with some embodiments of the present disclosure.In some embodiments, the illustrative steps of flow diagram 4200 may aidin conditioning a physiological signal by adjusting a baseline, scalingat least some peaks, or both. In some embodiments, the illustrativesteps of flow diagram 4200 may aid in reducing the effects of lowfrequency components (e.g., a constant or drifting baseline, peakamplitude changes) during subsequent processing of the window of data.

Step 4202 may include processing equipment receiving a window of data,derived from a physiological signal. In some embodiments, the window mayinclude a particular time interval (e.g., the most recent six seconds ofa processed physiological signal). Step 4202 may include pre-processing(e.g., using pre-processor 320) the output of a physiological sensor,and then storing a window of the processed data in any suitable memoryor buffer (e.g., QSM 72 of FIG. 2), for further processing by theprocessing equipment. In some embodiments, the window of data may berecalled from data stored in memory (e.g., RAM 54 of FIG. 2 or othersuitable memory) for subsequent processing. In some embodiments, thewindow size (e.g., the number of samples or time interval of data to bebuffered) of data is selected to capture multiple periods of oscillatoryphysiological activity. Panel 4210 shows an illustrative window ofdata42 derived from a physiological signal.

Step 4204 may include the processing equipment generating an envelope(e.g., varying upper and lower limits) based on the window of data ofstep 4202. The envelope may include an upper trace, which may be anoutline of the peak data values, and a lower trace, which may be anoutline of the valley data values. In some embodiments, the upper andlower traces may be generated using a mathematical formalism such as,for example, a linear or spline fit through the data points. The upperand lower traces may coincide with all, some, or no data points,depending on the enveloping technique used. Any suitable technique maybe used to generate an envelope of the window of data. Panel 4210 showsan illustrative envelope 4214 generated for window of data 4212.

Step 4206 may include the processing equipment modifying thephysiological data of step 4202 based on the envelope of step 4204. Insome embodiments, step 4206 may include generating a new window of data.For example, the midpoint of the difference between the lower and uppertrace at each location may be set as a new origin (e.g., a baseline).The upper and lower traces may then be scaled to respective desiredvalues at each point (e.g., 1 and 0, respectively, or 1 and −1,respectively) and the window of data may be similarly amplitude scaledat each point. In a further example, the lower trace may be subtractedfrom the window of data. The window of data may then be scaled torespective desired values at each point (e.g., to between 2 and 0) basedon the difference between the lower and upper traces. The mean may thenbe subtracted, centering the window of data about zero. Referencing Eq.25 below, either of the two previous examples gives a modified window ofdata M_(−1,1)(x_(i)) for each data point i at data point location x_(i),ranging from −1 to 1, in which f(x_(i)) is the initial window of data,g(x_(i)) is the lower trace, and h(x_(i)) is the upper trace.Referencing Eq. 26 below, either of the two previous examples gives amodified window of data M_(0,1)(x_(i)) for each data point i at datapoint location x_(i), ranging from 0 to 1, in which f(x_(i)) is theinitial window of data, g(x_(i)) is the lower trace, and h(x_(i)) is theupper trace. Any suitable mathematical formula, such as Eqs. 25 or 26,or any other suitable equation, may be used to modify a window of databased on an envelope.

$\begin{matrix}{{M_{{- 1},1}\left( x_{i} \right)} = {\frac{2\left( {{f\left( x_{i} \right)} - {g\left( x_{i} \right)}} \right)}{{h\left( x_{i} \right)} - {g\left( x_{i} \right)}} - 1}} & (25) \\{{M_{0,1}\left( x_{i} \right)} = \frac{{f\left( x_{i} \right)} - {g\left( x_{i} \right)}}{{h\left( x_{i} \right)} - {g\left( x_{i} \right)}}} & (26)\end{matrix}$In some embodiments, the processing equipment may down-weight outlierpoints in the buffered window of data. In some embodiments, theprocessing equipment may down-weight one or more points at each end ofthe buffered window of data.

Panel 4220 shows an illustrative modified window of data 4222, scaled torange from −1 to 1, centered about zero. Note that the lower and uppertraces have been scaled to lie horizontal at −1 and 1, respectively, andwindow of data 4222 has been scaled accordingly. As compared to windowof data 4212, modified window of data 4222 is shown to have a baselineof zero (rather than the trending baseline of window of data 4212), anda more consistent range of peak and valley values. Modified window ofdata 4222 may be used in any of the disclosed operation Modes, or anyother suitable processes accepting a window of data derived from aphysiological signal as an input.

FIG. 43 is a flow diagram 4300 of illustrative steps for modifyingphysiological data by subtracting a trend, in accordance with someembodiments of the present disclosure. In some embodiments, theillustrative steps of flow diagram 4300 may aid in conditioning aphysiological signal by modifying a baseline. In some embodiments, theillustrative steps of flow diagram 4300 may aid in reducing the effectsof low frequency components (e.g., a constant or drifting baseline)during subsequent processing of the window of data.

Step 4302 may include processing equipment buffering a window of data,derived from a physiological signal. In some embodiments, the window mayinclude a particular time interval (e.g., the most recent six seconds ofa processed physiological signal). Step 4302 may include pre-processing(e.g., using pre-processor 320) the output of a physiological sensor,and then storing a window of the processed data in any suitable memoryor buffer (e.g., queue serial module 72 of FIG. 2), for furtherprocessing by the processing equipment. In some embodiments, the windowof data may be recalled from data stored in memory (e.g., RAM 54 of FIG.2 or other suitable memory) for subsequent processing. In someembodiments, the window size (e.g., the number of samples or timeinterval of data to be buffered) of data is selected to capture multipleperiods of oscillatory physiological activity.

Step 4304 may include the processing equipment generating a signal basedon the window of data of step 4302. The generated signal may represent abaseline or otherwise a trend in the window of data of step 4302. Insome embodiments, the signal generated at step 4304 may include a movingmean, a linear fit (e.g., from a linear regression), a quadratic fit,any other suitable polynomial fit (e.g., using a “least-squares”regression), any other suitable functional fit (e.g., exponential,logarithmic, sinusoidal), any piecewise combination thereof, or anyother combination thereof. Any suitable technique for generating asignal indicative of a trend may be used at step 4304. In someembodiments, each data point within the window of data may be givenequal weighting. In some embodiments the processing equipment maydown-weight outlier points or one or more points at each end of thebuffered window of data.

Step 4306 may include the processing equipment subtracting the generatedsignal of step 4304 from the window of data of step 4302. In someembodiments, step 4306 may include generating a new window of data(e.g., the window data of step 4302 with the signal of step 4304removed), which may be further processed, stored in any suitable memory,or both. Subtraction of the signal of step 4304 from the window of dataof step 4302 may aid in processing the data for rate information byremoving low frequency components. Further illustration of the signalsubtraction of flow diagram 4300 is provided by FIGS. 44-46.

FIG. 44 is a plot 4400 of an illustrative window of data 4402 with themean removed, in accordance with some embodiments of the presentdisclosure. The abscissa of plot 4400 is presented in units proportionalto sample number, while the ordinate is presented in arbitrary units,with zero notated. Window of data 4402 is shown to include an increasingbaseline, even with the mean value of the data removed. Accordingly,subtraction of the mean may provide unsatisfactory results when usedwith data having a changing baseline. In some such circumstances, alinear baseline, or other suitable trending baseline, may be subtractedfrom the window of data rather than a mean value. In some cases, inwhich the baseline of a window of data may be relatively constant, amean subtraction may be preferred to a more complex baseline fit.

FIG. 45 is a plot 4500 of an illustrative window of data 4502 and aquadratic fit 4504, in accordance with some embodiments of the presentdisclosure. The abscissa of plot 4500 is presented in units proportionalto sample number, while the ordinate is presented in arbitrary units. Ascompared to FIG. 44, quadratic fit 4504 follows the trending baseline ofwindow of data 4502 more closely than a mean subtraction would becapable of FIG. 46 shows a plot 4600 of modified window of data 4602derived from illustrative window of data 4502 of FIG. 45 with quadraticfit 4504 subtracted, in accordance with some embodiments of the presentdisclosure. The abscissa of plot 4600 is presented in units proportionalto sample number, while the ordinate is presented in arbitrary units,with zero notated. Modified window of data 4602 is substantiallycentered about zero, with a relatively constant baseline of zero.

FIG. 47 is a plot 4700 of an illustrative modified window of data 4702derived from an original window of data with the mean subtracted, inaccordance with some embodiments of the present disclosure. The abscissaof plots 4700, 4800, and 4900 of FIGS. 47-49 are presented in unitsproportion to time (or sample number), while the ordinate is presentedin arbitrary units, with zero notated. Modified window of data 4702exhibits significant low frequency activity that is not substantiallyreduced nor eliminated by mean subtraction. Accordingly, modified windowof data 4702 may present challenges for some techniques for extractingrate information. Note that modified window of data 4702 spans seventick marks along the ordinate, in arbitrary units.

FIG. 48 is a plot 4800 of an illustrative modified window of data 4802derived from the same original window of data as FIG. 47 with a linearbaseline subtracted, in accordance with some embodiments of the presentdisclosure. Note that modified window of data 4802 spans almost six tickmarks along the ordinate, in similar units of plot 4700, and accordinglyexhibits relatively less (e.g., smaller amplitude) low frequencyactivity than modified window of data 3802.

FIG. 49 is a plot 4900 of an illustrative modified window of data 4902derived from the same original windows of data as FIGS. 47 and 48 with aquadratic baseline subtracted, in accordance with some embodiments ofthe present disclosure. Note that modified window of data 4902 spansalmost four tick marks along the ordinate, in similar units of plots4700 and 4800, and accordingly exhibits relatively less (e.g., smalleramplitude) low frequency activity than either of modified windows ofdata 4702 and 4802. In some circumstances, the subtraction of aquadratic fit may provide better results than a mean subtraction orlinear subtraction. In some circumstances, the subtraction of a higherorder polynomial fit, or any other suitable fit, may provide betterresults than a mean subtraction, linear subtraction, or quadraticsubtraction. In some circumstances, a relatively simpler baseline fitmay be preferred to a more complex baseline fit.

In some embodiments, processing equipment may apply a derivative limiterto a window of data, or signal derived thereof. A physiological pulsemay be expected to exhibit oscillatory behavior, while baseline shiftsor other non-oscillatory behavior may likely be attributable to noise(e.g., subject movement, electromagnetic interference). Further, thefirst derivative of a primarily oscillatory signal may also exhibitoscillatory behavior. Relatively large baseline shifts may beaccompanied by a corresponding increase in the value of the derivativeof the data. The baseline shift may be observable as a relatively largerpeak (positive or negative) in the first derivative as compared to otherpeaks in the first derivative corresponding to purely oscillatoryactivity of the signal. In some embodiments, a comparison of thedifferences or derivatives of physiological data against a suitablethreshold may be used as a noise metric. For example, values of thephysiological data falling below a suitable threshold value may beconsidered to exhibit low noise. Similarly, values of the physiologicaldata above a suitable threshold value may be considered to exhibit highnoise. In some embodiments, a stability function such as, for example, aLyapunov function that relates to oscillator stability, may be used toidentify noise in physiological data. In some embodiments, theidentification of high positive or negative slopes or noise can be usedto limit derivative values in the window of data. The followingdiscussion in the context of FIGS. 50-55 provides some illustrativetechniques for applying a derivative limiter to physiological data.

FIG. 50 is a flow diagram 5000 of illustrative steps for modifyingphysiological data using a derivative limiter, in accordance with someembodiments of the present disclosure. The illustrative techniques offlow diagram 5000 may be used to modify localized sections of a bufferof data. When there is a high slope or baseline shift in the buffer ofdata, bandpass filters and polynomial de-trending, for example, may notadequately remove the artifact without distorting the surrounding data.In some circumstances, a relatively noise-free PPG signal likely has acertain range of slopes or differences. If there are slopes ordifferences outside of the expected range, then under some circumstancesit may be assumed that they are due to noise, and the physiological datamay be modified accordingly. FIG. 51 is a panel of three plots showingan illustrative window of data having a baseline shift, a firstderivative of the window of data, and a modified window of data, inaccordance with some embodiments of the present disclosure. FIG. 51 willbe referred to below during the discussion of the illustrative steps offlow diagram 5000.

Step 5002 may include processing equipment receiving physiological data,for example, by buffering a window of data derived from a physiologicalsignal. In some embodiments, the physiological data may include aparticular time interval of samples (e.g., the most recent six secondsof a processed physiological signal). Step 5002 may includepre-processing (e.g., using pre-processor 320) the output of aphysiological sensor, and then storing a window of the processed data inany suitable memory or buffer (e.g., queue serial module 72 of FIG. 2),for further processing by the processing equipment. In some embodiments,the window of data may be recalled from data stored in memory (e.g., RAM54 of FIG. 2 or other suitable memory) for subsequent processing. Insome embodiments, the window size (e.g., the number of samples or timeinterval of data to be buffered) of data is selected to capture multipleperiods of oscillatory physiological activity. In some embodiments, thereceived physiological data may have previously undergone de-trending(e.g., polynomial de-trending using any suitable order polynomial) orother signal conditioning. Plot 5100 of FIG. 51 shows an illustrativewindow of data 5102 exhibiting a baseline shift.

Step 5004 may include processing equipment calculating differencesbetween adjacent samples of the physiological data. In some embodiments,the processing equipment may perform a subtraction between values ofadjacent samples. In some embodiments, the processing equipment maycalculate the differences by calculating a first derivative of thephysiological data. The processing equipment may calculate a series ofdifferences between each adjacent pair of samples, and may accordinglygenerate a difference signal using the calculated differences. Forexample, the processing equipment may compute forward differences,backward differences, or central differences between each pair ofadjacent points to generate a difference signal. In a further example,the processing equipment may compute a numerical derivative at eachpoint in the data, generating a difference signal. Any suitabledifference technique may be used by the processing equipment.

Step 5006 may include processing equipment determining upper and lowerthresholds for the differences calculated at step 5004. The upper andlower thresholds may be determined based on the difference valuesthemselves. For example, a threshold may be determined using thestandard deviation of the physiological data, difference signal, orboth. As shown in Eq. 27:thresholds=±Kσ  (27)upper and lower thresholds may be proportional by proportionalityconstant K to the standard deviation σ of the differences. The upper andlower thresholds need not be symmetrical nor constant, and may each haveunique K values. For example, pulses in a PPG signal are typicallyasymmetrical and the upper and lower K values may be different toreflect the different expected positive and negative differences. Plot5110 of FIG. 51 shows difference signal 5112 (i.e., a calculatedderivative of the illustrative window of data 5102), computed using aforward difference (although any suitable numerical difference ordifferentiation may be used). In some embodiments, thresholds may bedetermined based on one or more peaks in the difference signal. Forexample, in some embodiments, an upper threshold may be determined basedon the height of all peaks excluding the highest peak (e.g., thethreshold may be 1.5 times the average of the peaks heights excludingthe highest peak). Any suitable upper and lower threshold may be used inaccordance with the present disclosure. In some embodiments, a thresholdmay be based on algorithm settings. For example, if stronger de-trendingis selected, the threshold values may be tightened (e.g., thresholdsbecome relatively closer which makes exceeding them more likely).

Step 5008 may include processing equipment identifying differences ofstep 5004 that exceed the thresholds of step 5006. The difference signalmay be compared to the upper and lower thresholds, and values exceedingthe threshold range may be identified. Plot 5110 of FIG. 51 shows thedifference signal 5112 of the data of plot 5100 along with upper andlower threshold values, T+ and T−, respectively). The highest peak inthe difference signal corresponds to the baseline offset shown in plot5100. The points of intersection of the highest peak with the upperthreshold are shown by the vertical dashed lines. The difference signalcrosses the upper threshold T+ due to the positive baseline shift. Asimilar negative baseline shift would result in a negative peak (atrough) which would cross the lower threshold. The processing equipmentmay identify a sample number, time value, coordinate pair, any othersuitable description of a point, or any combination thereof for samplesthat exceed a threshold. In some embodiments, the processing equipmentmay identify the start and end of when the difference signal firstexceeds a threshold.

Step 5010 may include processing equipment determining one or moreoffset values based on the identified differences of step 5008. Step5012 may include processing equipment modifying the physiological databased on the one or more offset values determined at step 5010. In someembodiments, once the offset is subtracted, the processing equipment maybandpass filter the resulting signal, de-trend the resulting signal(e.g., polynomial de-trending using any suitable order polynomial), orboth. Plot 5120 of FIG. 51 shows modified data 5122, derived from thephysiological data of panel 5100, and modified using the thresholdcrossings of plot 5110. In the illustrated example of plot 5120 of FIG.51, values are held constant for points corresponding to differencevalues exceeding the threshold, and an offset is applied at therightmost threshold crossing to make the modified data continuous. Itcan be seen from a comparison of plots 5100 and 5120 of FIG. 51 that theillustrative techniques of flow diagram 5000 aid in reducing thebaseline shift of the physiological data, and accordingly aid insubsequent processing.

The processing equipment may use any suitable offset and modification inthe context of flow diagram 5000 of FIG. 50. In an illustrative example,the processing equipment may make adjacent values whose correspondingdifference exceeds a threshold equal to each other (i.e., made to have adifference of 0). In a further illustrative example, the processingequipment may remove one or more of the values and shift remaining leftor right portion of the data up or down. In a further illustrativeexample, the processing equipment may shift the left or right portion upor down to decrease the difference between the adjacent values (thedifference can be a difference corresponding to the threshold ordetermined based on adjacent differences). In a further illustrativeexample, the processing equipment may add hysteresis to themodifications (e.g., confirm x number of subsequent differences arebelow threshold and if not, continue holding the previous value orcontinue current modification being used). In a further illustrativeexample, the processing equipment may smooth out the difference thatexceeds a threshold by modifying not only the difference that exceededthe threshold, but also adjacent differences to smooth out themodification (e.g., the values and the first derivatives may be matchedat each threshold crossing to smooth the resulting transitions).

FIG. 52 is a flow diagram 5200 of illustrative steps for modifyingphysiological data using a stability function, in accordance with someembodiments of the present disclosure. In some embodiments, thestability function may be used to scale physiological data for which thebaseline varies over time (e.g., at a characteristic time scale largerthan the period of the physiological pulse). In some embodiments, thefirst derivative of the stability function may be analyzed rather than adifference signal (e.g., as discussed in the context of flow diagram5000 of FIG. 50) to identify and/or modify noisy portions ofphysiological data. FIG. 53 is a panel of three plots 5300, 5310, and5320 showing an illustrative window of data 5302, a stability function5312, and a derivative of the stability function 5322, respectively, inaccordance with some embodiments of the present disclosure. FIG. 53 willbe referred to below during the discussion of the illustrative steps offlow diagram 5200.

Step 5202 may include processing equipment receiving physiological data,for example, by buffering a window of data derived from a physiologicalsignal. In some embodiments, the physiological data may include aparticular time interval of samples (e.g., the most recent six secondsof a processed physiological signal). Step 5202 may includepre-processing (e.g., using pre-processor 320) the output of aphysiological sensor, and then storing a window of the processed data inany suitable memory or buffer (e.g., queue serial module 72 of FIG. 2),for further processing by the processing equipment. In some embodiments,the window of data may be recalled from data stored in memory (e.g., RAM54 of FIG. 2 or other suitable memory) for subsequent processing. Insome embodiments, the window size (e.g., the number of samples or timeinterval of data to be buffered) of data is selected to capture multipleperiods of oscillatory physiological activity. In some embodiments, thereceived physiological data may have previously undergone de-trending(e.g., polynomial de-trending using any suitable order polynomial) orother signal conditioning. Panel 5300 shows an illustrative window ofphysiological data exhibiting a varying baseline.

Step 5204 may include processing equipment generating a stability signalbased on the physiological data. In some embodiments, the stabilitysignal may be a Lyapunov function, for example, generated using thefollowing Eq. 28:

$\begin{matrix}{L = {\left( \frac{\mathbb{d}x}{\mathbb{d}t} \right)^{2} + x^{2}}} & (28)\end{matrix}$where x are the sample values, dx/dt are the derivative values(calculated using any suitable numerical or analytical method), and L isthe Lyapunov function. Panel 5310 shows a Lyapunov function generatedfrom the physiological data using Eq. 28. Step 5206 may includeprocessing equipment generating a derivative of the stability signal.Panel 5320 shows a first derivative of the Lyapunov function of panel5310. Typically, Lyapunov functions are used to analyze the stability ofa linear or non-linear dynamic system. The system under test isconsidered stable when the time derivative of the Lyapunov function iszero. In the context of a physiological signal, the time derivative of aLyapunov function may provide an indication of the noise level in thephysiological data.

Step 5208 may include processing equipment comparing the derivative ofthe stability function of step 5206 with a threshold. The derivative ofthe stability function may provide an indication of the noise level inthe physiological data. In some embodiments, the processing equipmentmay generate the threshold at step 5208. For example, the threshold mayinclude one or more predetermined values, which may be constant orvariable. In a further example, the processing equipment may determinethe threshold based on the standard deviation of the derivative of thestability function (e.g., the threshold may be equal to a multiple ofthe standard deviation). In some embodiments, the processing equipmentmay identify threshold crossings of the derivative of the stabilityfunction, if any.

Step 5210 may include processing equipment identifying relatively noisyportions of the physiological data based on the comparison of step 5208.Portions of the derivative of the stability signal may be identifiedbased on one or more threshold crossings. For example, the portion ofthe derivative of the stability function between a pair of thresholdcrossings may be identified as relatively noisy. In a further example,every point of the derivative of the stability function exceeding thethreshold may be identified as relatively noisy.

Step 5212 may include processing equipment modifying the physiologicaldata based on the noisy portions identified at step 5210. In someembodiments, the processing equipment may determine portions of thederivative of the stability signal outside of threshold values (e.g.,between a pair of threshold crossings) and limit the derivative valuesof the physiological data to one or more threshold value(s) in thoseportions.

FIG. 54 is a flow diagram 5400 of illustrative steps for modifyingphysiological data using a corrected difference signal, in accordancewith some embodiments of the present disclosure. FIG. 55 is a panel ofplots showing an illustrative difference signal, a sorted differencesignal, a corrected sorted difference signal, and a corrected differencesignal, in accordance with some embodiments of the present disclosure.FIG. 55 will be referred to below during the discussion of theillustrative steps of flow diagram 5400.

Step 5402 may include processing equipment receiving physiological datafrom a physiological sensor, memory, any other suitable source, or anycombination thereof. For example, referring to system 300 of FIG. 3, theprocessing equipment may receive a window of physiological data frominput signal generator 310. Sensor 318 of input signal generator 310 maybe coupled to a subject, and may detect physiological activity such as,for example, RED and/or IR light attenuation by tissue, using aphotodetector. In some embodiments, physiological signals generated byinput signal generator 310 may be stored in memory (e.g., RAM 54 of FIG.2, QSM 72 and/or other suitable memory) after being pre-processed bypre-processor 320. In such cases, step 5402 may include recalling datafrom the memory for further processing.

Step 5404 may include processing equipment generating a differencesignal (e.g., by calculating a sequence of difference values betweenadjacent samples of the physiological data). In some embodiments, theprocessing equipment may perform a subtraction between values ofadjacent samples. In some embodiments, the processing equipment maycalculate the differences by calculating a first derivative of thephysiological data. For example, the processing equipment may computeforward differences, backward differences, or central differencesbetween each pair of adjacent points to generate a difference signal. Ina further example, the processing equipment may compute a numericalderivative at each point in the data, generating a difference signal.Any suitable difference technique may be used by the processingequipment to generate the difference signal. Panel 5500 shows anillustrative difference signal 5502 generated from a window ofphysiological data. Note that region 5504 exhibits a relatively largepeak and trough, typically indicative of noise.

Step 5406 may include processing equipment sorting the difference valuesof step 5404, while keeping track of the original position of eachsorted point. In some embodiments, an index or other identifier may beused to retain the original position information of the sorteddifference values. The processing equipment may sort the values inascending or descending order. Referencing sorted values in ascendingorder, the most negative values come first followed by less negativevalues, positive values, and finally larger positive values.

Step 5408 may include processing equipment fitting one or more lines toone or more portions of the sorted difference signal of step 5406. Insome embodiments, the processing equipment may perform a linearregression (e.g., a least-squares regression or a weighted least-squaresregression) using at least a portion of the sorted difference signal. Insome embodiments, the processing equipment may fit a line using everypoint of the sorted difference signal. In some embodiments, theprocessing equipment may fit a line using a portion of the sorteddifference signal. For example, the processing equipment may omit one ormore points at one or both ends of the sorted difference signal whendetermine the line fit. In some embodiments, the line fit may include aslope value and an ordinate intercept value (e.g., using the y=mx+blinear form where m is the slope and b is the intercept).

Step 5410 may include processing equipment determining upper and lowerthresholds for the sorted difference signal of step 5406. The upper andlower thresholds may be determined based on the difference valuesthemselves. For example, a threshold may be determined using thestandard deviation of the physiological data, difference signal, orboth. In an illustrative example, Eq. 27 may be applied, in which theupper and lower thresholds may be proportional by proportionalityconstant K to the standard deviation σ of the difference signal. Theupper and lower thresholds need not be symmetrical nor constant, and mayeach have unique K values.

Step 5412 may include processing equipment identifying points of thesorted difference signal of step 5406 that exceed the thresholds of step5410. The difference signal may be compared to the upper and lowerthresholds, and values exceeding the threshold range may be identified.Panel 5510 of FIG. 55 shows a sorted difference signal 5512 derived fromdifference signal 5502 of panel 5500, along with line fit 5518, andrespective upper and lower thresholds 5514 and 5516. The pointsassociated with the peak and trough in region 5504 of difference signal5502 are evident as the relatively large positive values and largenegative values at the ends of sorted difference signal 5512. Sorteddifference signal 5512 is shown to exceed thresholds 5514 and 5516, andaccordingly points outside of the thresholds may be identified. Theprocessing equipment may identify a sample number, time value,coordinate pair, any other suitable description of a point, or anycombination thereof that correspond to points exceeding the upper orlower thresholds.

It will be understood that the steps 5408, 5410, and 5412 are merelyillustrative, and any suitable technique for identifying points may beused. For example, any of the illustrative techniques described in thecontext of FIGS. 30 and 32 may be used.

Step 5414 may include processing equipment modifying the points of thesorted difference signal identified at step 5412. In some embodiments,the processing equipment may set each sorted difference value outside ofthe thresholds equal to the threshold, thus limiting large differencevalue excursions. In some embodiments, the processing equipment mayapply a transition function to force the sorted difference signal to thenearest threshold asymptotically. Any suitable technique may be used bythe processing equipment to modify one or more points to reduceexcursions of the sorted difference signal outside of the thresholds.For example, in some embodiments, the processing equipment may modifythe one or more values by setting them equal to the same value as thelast point that was within the threshold. In a further example, theprocessing equipment may modify the one or more values by setting themequal to a predetermined value such as zero or any other suitable valueless than the threshold. Panel 5520 of FIG. 55 shows a modified sorteddifference signal 5522 derived from sorted difference signal 5512 ofpanel 5510. In order to generate modified sorted difference signal 5522,the values of sorted difference signal 5512 exceeding the thresholds, asshown in panel 5510, have been set equal to the appropriate thresholdvalues. The ends of modified sorted difference signal 5522 are shown tobe substantially linear (e.g., similar to thresholds 5514 and 5516), andexhibit relatively smaller difference values.

Step 5416 may include processing equipment reordering the modifiedsorted difference signal of step 5414 based on the original positions ofeach data point. The output from performing step 5416 may be a modifieddifference signal, with the largest difference values (positive and/ornegative) modified to relatively smaller values. Panel 5530 of FIG. 55shows a modified difference signal 5532 derived from modified sorteddifference signal 5522 of panel 5520. As compared to difference signal5502 of panel 5500, modified difference signal 5532 exhibits, forexample, a relatively smaller peak and trough in region 5534, whichcorresponds to region 5504 of panel 5500.

Step 5418 may include processing equipment integrating the modifieddifference signal of step 5416 to generate a filtered physiologicalsignal. In some embodiments, the processing equipment may use anysuitable analytic integral (e.g., using a functional fit to a modifieddifference signal), numerical integral (e.g., Euler's method,Runge-Kutta method, a predictor-corrector method, or any other suitabletechnique of any suitable order), any other suitable technique, or anycombination thereof. For example, the processing equipment may use afirst value of zero, and generate each subsequent value by adding thecorresponding difference value to the preceding value, and then apply abaseline offset equivalent to the mean of the original physiologicaldata. In a further example, the processing equipment may use anEuler-trapezoid predictor-corrector technique to perform the integral ofstep 5418.

In some embodiments, processing equipment may apply a normalization to awindow of data, or signal derived thereof. A physiological pulse may beexpected to exhibit oscillatory behavior, while baseline shifts or othernon-oscillatory behavior may likely be attributable to noise (e.g.,subject movement, electromagnetic interference). Further, changes in theamplitude of oscillatory activity may be undesirable. In someembodiments, a window of physiological data may be partitioned into apositive signal and a negative signal, which may each be furtherprocessed and combined to modify the physiological data. In someembodiments, a filtered signal may be used to modify the physiologicaldata. The following discussion in the context of FIGS. 56-59 providessome illustrative techniques for normalizing to physiological data.

FIG. 56 is a flow diagram 5600 of illustrative steps for modifyingphysiological data using a positive signal and a negative signal, inaccordance with some embodiments of the present disclosure. FIG. 57 is apanel of five plots showing an illustrative window of physiological datahaving varying amplitude, filtered signals, combined signals, and amodified window of data, in accordance with some embodiments of thepresent disclosure. FIG. 57 will be referred to below during thediscussion of the illustrative steps of flow diagram 5600.

Step 5602 may include processing equipment receiving physiological datafrom a physiological sensor, memory, any other suitable source, or anycombination thereof. For example, referring to system 300 of FIG. 3, theprocessing equipment may receive a window of physiological data frominput signal generator 310. Sensor 318 of input signal generator 310 maybe coupled to a subject, and may detect physiological activity such as,for example, RED and/or IR light attenuation by tissue, using aphotodetector. In some embodiments, physiological signals generated byinput signal generator 310 may be stored in memory (e.g., RAM 54 of FIG.2, QSM 72 and/or other suitable memory) after being pre-processed bypre-processor 320. In such cases, step 5602 may include recalling datafrom the memory for further processing.

Step 5604 may include processing equipment generating a positive signalbased on the physiological data of step 5602. In some embodiments, theprocessing equipment may generate the positive signal from the positivevalues of the physiological data of step 5602. In some embodiments, theprocessing equipment may insert zeros in the positive signal where thephysiological data is negative. Step 5608 may include processingequipment generating a negative signal based on the physiological dataof step 5602. In some embodiments, the processing equipment may generatethe negative signal from the negative values of the physiological dataof step 5602. In some embodiments, the processing equipment may insertzeros in the negative signal where the physiological data is positive.For example, plot 5700 of FIG. 57 shows positive signal 5702 andnegative signal 5704 generated from the same physiological data. Thephysiological data exhibits a relatively large peak and trough in region5706. Further processing of such physiological data may benefit fromnormalization techniques, which may aid in reducing relatively largeexcursions in the physiological data.

Step 5606 may include processing equipment filtering the positive signalof step 5604. In some embodiments, the processing equipment may apply alow pass filter to the positive signal. Any suitable LPF, having anysuitable cutoff and spectral character (e.g., Butterworth filters,Chebyshev filters, Bessel filters, RC filters, or other suitablefilter), may be used at step 5606. Step 5610 may include processingequipment filtering the negative signal of step 5608. In someembodiments, the processing equipment may apply a low pass filter to thenegative signal. Any suitable LPF, having any suitable cutoff andspectral character (e.g., Butterworth filters, Chebyshev filters, Besselfilters, RC filters, or other suitable filter), may be used at step5610. For example, plot 5710 of FIG. 57 shows illustrative filteredpositive signal 5712 and filtered negative signal 5714.

Step 5612 may include processing equipment combining the filteredpositive signal and the filtered negative signal of respective steps5606 and 5610. In some embodiments, the processing equipment may sum thefiltered positive signal and the filtered negative signal of respectivesteps 5606 and 5610. In some embodiments, the processing equipment maysum the filtered positive signal and the filtered negative signal ofrespective steps 5606 and 5610 using weights. The processing equipmentmay apply any suitable combination technique at step 5612 to combine thefiltered positive signal and the filtered negative signal of respectivesteps 5606 and 5610. For example, plot 5720 of FIG. 57 showsillustrative combined signal 5722, generated by adding filtered positivesignal 5712 and filtered negative signal 5714 of plot 5710.

Step 5614 may include processing equipment modifying the physiologicaldata of step 5602 based on the combined signal of step 5612. In someembodiments, the combined signal may be subtracted from thephysiological data, which may aid in reducing relatively largeexcursions in the physiological data. In some embodiments, the combinedsignal may be further modified and then subtracted from thephysiological data. For example, plot 5720 of FIG. 57 shows illustrativecombined signal 5722, along with thresholds 5726 and 5728 generated bythe processing equipment. The thresholds may be constant values,variable values, based on the properties of the physiological data(e.g., the positive and negative thresholds may be equal to the standarddeviation of the physiological data), a signal derived thereof (e.g.,the positive and negative thresholds may be equal to the averages of therespective positive and negative signals), any other suitableinformation, or any combination thereof. Plot 5730 of FIG. 57 showsillustrative combined signal 5722, and scaled combined signal 5732,generated from scaling combined signal 5722 based on thresholds 5726 and5728. The scaling may be linear, polynomial of order two or greater,exponential, logarithmic, any other function scaling, any other suitablescaling, or any combination thereof. For example, the processingequipment may apply any of illustrative Eqs. 29-30 shown below:

$\begin{matrix}{M_{i} = {{KC}_{i}\left( \frac{C_{i}}{T_{j}} \right)}^{n}} & (29) \\{M_{i} = {{KC}_{i}{\mathbb{e}}^{A{({C_{i} - T_{j}})}}}} & (30)\end{matrix}$to the combined signal (having values C_(i) for index i) to generate amodified combined signal (having values M_(i) for index i) at step 5614.Note that in Eqs. 29-30, T_(j) represents an appropriate threshold value(e.g., positive threshold for positive values, negative threshold fornegative values), while K, A, and n each represent an adjustableconstant. For example, referencing Eqs. 29-30, the constant K may be setto a value of one, so that the combined signal and modified combinedsignal are equal at the threshold crossings (e.g., as shown in plot5730). Plot 5740 shows the original physiological data 5744 and modifiedphysiological data 5742, generated by subtracting modified combinedsignal 5732 from original physiological data 5744. Modifiedphysiological data 5742 exhibits relatively reduced excursions in region5706 as compared to original physiological data 5744.

FIG. 58 is a flow diagram 5800 of illustrative steps for modifyingphysiological data using a filtered signal, in accordance with someembodiments of the present disclosure. FIG. 59 is a panel of six plotsshowing an illustrative window of physiological data, an absolute valuesignal, a filtered signal, a shifted signal, and a modified window ofdata, in accordance with some embodiments of the present disclosure.FIG. 59 will be referred to below during the discussion of theillustrative steps of flow diagram 5800.

Step 5802 may include processing equipment receiving physiological datafrom a physiological sensor, memory, any other suitable source, or anycombination thereof. For example, referring to system 300 of FIG. 3, theprocessing equipment may receive a window of physiological data frominput signal generator 310. Sensor 318 of input signal generator 310 maybe coupled to a subject, and may detect physiological activity such as,for example, RED and/or IR light attenuation by tissue, using aphotodetector. In some embodiments, physiological signals generated byinput signal generator 310 may be stored in memory (e.g., RAM 54 of FIG.2, QSM 72 and/or other suitable memory) after being pre-processed bypre-processor 320. In such cases, step 5802 may include recalling datafrom the memory for further processing. For example, plot 5900 of FIG.59 shows illustrative data 5902 exhibiting a significant change inamplitude.

Step 5804 may include processing equipment calculating absolute valuesof the physiological data of step 5802. The result of step 5804 may be asequence of absolute values, having only positive values (and possiblyzeros). For example, plot 5910 of FIG. 59 shows absolute values 5912derived from illustrative data 5902.

Step 5806 may include processing equipment filtering the absolute valuesof step 5804. In some embodiments, the processing equipment may apply alow pass filter to the absolute values of step 5804. Any suitable LPF,having any suitable cutoff and spectral character (e.g., Butterworthfilters, Chebyshev filters, Bessel filters, RC filters, or othersuitable filter), may be used at step 5806. For example, plot 5920 ofFIG. 59 shows filtered values 5922 derived from absolute values 5912.The illustrative LPF used to generate filtered values has the form ofEqs.F ₁ =Z ₁  (31)F _(i) =aZ _(i) +bZ _(i-1)  (32)in which Z_(i) is the unfiltered signal (i.e., the absolute values),F_(i) is the filtered signal, using index i, and filter coefficients aand b (which may sum to one). It will be understood that any suitableLPF technique (e.g., a direct form II transpose structure) may beapplied by the processing equipment to generate filtered values at step5806.

Step 5808 may include processing equipment modifying the filtered valuesof step 5806. In some embodiments, the processing equipment may modifythe filtered values by scaling, shifting, or both. For example, plot5930 of FIG. 59 shows subtracted values 5932 derived from filteredvalues 5922. Subtracted values 5932 are derived from filtered values5922 by subtracting the minimum value of filtered values 5922, excludingend portions, from all values of filtered values 5922. Accordingly,subtracted values 5932 are down-shifted values of filtered values 5922.Further, plot 5940 of FIG. 59 shows modified values 5942 derived fromsubtracted values 5932 by adding a constant gain value to each ofsubtracted values 5932. In some embodiments, a subtraction and a gainvalue addition may be performed, although these operations may becondensed into a single value shift. Modified values, such as modifiedvalues 5942, may be used to modify physiological data exhibitingsignificant amplitude changes.

Step 5810 may include processing equipment dividing the receivedphysiological data of step 5802 by the modified filtered values of step5808. For example, the processing equipment may modify filter values asshown in Eq. 33, and modify the physiological data of step 5802 usingEq. 34:

$\begin{matrix}{M_{i} = {F_{i} - K_{1} + K_{2}}} & (33) \\{Y_{i} = \frac{X_{i}}{M_{i}}} & (34)\end{matrix}$in which F_(i) are the filtered values, K₁ and K₂ are the shifts, M_(i)are the modified filtered values, X_(i) are the physiological datavalues (as received at step 5802), and Y_(i) are the modifiedphysiological data values, all for index i that may range from 1 to Nfor N data points. Plot 5950 of FIG. 59 shows modified data 5952 derivedfrom data 5902 and modified filter values 5942 using Eq. 34. Theamplitude variation in modified data 5952 is relatively less than theamplitude variation in data 5002, and exhibits normalization indicativeof the illustrative techniques of flow diagram 5800.

FIG. 60 is a flow diagram 6000 of illustrative steps for selectivelyapplying a filter to physiological data, in accordance with someembodiments of the present disclosure.

Step 6002 may include processing equipment receiving physiological datafrom a physiological sensor, memory, any other suitable source, or anycombination thereof. For example, referring to system 300 of FIG. 3, theprocessing equipment may receive a window of physiological data frominput signal generator 310. Sensor 318 of input signal generator 310 maybe coupled to a subject, and may detect physiological activity such as,for example, RED and/or IR light attenuation by tissue, using aphotodetector. In some embodiments, physiological signals generated byinput signal generator 310 may be stored in memory (e.g., RAM 54 of FIG.2, QSM 72 and/or other suitable memory) after being pre-processed bypre-processor 320. In such cases, step 6002 may include recalling datafrom the memory for further processing.

Step 6004 may include processing equipment determining a metric based onthe received physiological signal of step 6002. For example, theprocessing equipment may determine the metric in accordance with any ofthe techniques discussed in the context of FIGS. 11-41, any othersuitable techniques, or any combination thereof. The determined metricmay be indicative of de-trending, noise, a physiological classification(e.g., presence of a dicrotic notch) any other value indicative of thephysiological data, any other signal conditioning property derived basedon the physiological data, or any combination thereof.

Step 6006 may include processing equipment selectively applying adigital filter, having at least two filter coefficients, to thephysiological data of step 6002 based on the metric of step 6004. Thefilter coefficients may correspond to a weighted sum of thephysiological signal and a difference signal that corresponds to thephysiological signal. For example, the digital filter may output filterdata F_(i) calculated using Eq. 35:F _(i) =C ₁ X _(i) +C ₂(X _(i) −X _(i-1))=aX _(i) +bX _(i-1)  (35)where X_(i) is a sample point of the physiological data, X_(i-1) is theprevious sample point of the physiological data, C₁ and C₂ arecoefficients, and a and b are the filter coefficients, which may assumeany suitable value. Any suitable difference calculation, or othernumerical derivative calculation, may be used such as, for example, abackward difference, a forward difference, or a central difference. Thedigital filter may be implemented as, for example, a finite impulseresponse (FIR) filter having at least two coefficients. Selectivelyapplying the digital filter based on the metric may provide a techniqueto reduce the effects of noise in the physiological data. For example,physiological data from a neonate may exhibit a relatively highfrequency component corresponding to a physiological rate, and arelatively low frequency component corresponding to noise. Applicationof the digital filter of step 6006 may reduce the low frequency noisecomponent in the filtered data, as the derivative of the low frequencynoise would be expected to be relatively less than the derivativeassociated with the higher frequency component. In some embodiments, theprocessing equipment may adjust the filter coefficients based on a noisemetric, calculated rate, or both. For example, at relatively greaterlevels of noise and at relatively higher rates (e.g., where there is alower probability of a dicrotic notch), the processing equipment maymore heavily weight the derivative (e.g., increase C₂). In someembodiments, for example, the digital filter of step 6006 may not beapplied when the physiological data is classified as having a dicroticnotch. The derivative of physiological data having a dicrotic notch maymake the dicrotic notch appear as an additional pulse. In a furtherexample, at lower physiological rates, the processing equipment may moreheavily weight the physiological signal relative to the derivative.

FIG. 61 is a flow diagram 6100 of illustrative steps for applying abandpass filter having adjustable settings to physiological data, inaccordance with some embodiments of the present disclosure. In somecircumstances, the processing equipment may apply a bandpass filter tophysiological data received over time. In order to reduce the likelihoodthat the bandpass filter is tuned to noise (as opposed to the correctrate), the processing equipment may determine one or more metricsindicative of the level of noise in the physiological data. Accordingly,under some circumstances, one or more settings of the bandpass filtermay be adjusted based on the one or more metrics, a calculated rate, orboth.

Step 6102 may include processing equipment receiving physiological datafrom a physiological sensor, memory, any other suitable source, or anycombination thereof. For example, referring to system 300 of FIG. 3, theprocessing equipment may receive a window of physiological data frominput signal generator 310. Sensor 318 of input signal generator 310 maybe coupled to a subject, and may detect physiological activity such as,for example, RED and/or IR light attenuation by tissue, using aphotodetector. In some embodiments, physiological signals generated byinput signal generator 310 may be stored in memory (e.g., RAM 54 of FIG.2, QSM 72 and/or other suitable memory) after being pre-processed bypre-processor 320. In such cases, step 6102 may include recalling datafrom the memory for further processing.

Step 6104 may include processing equipment determining a valueindicative of physiological rate based on the physiological data of step6102. In some embodiments, the processing equipment may use any of thetechniques disclosed herein to calculate a physiological rate (e.g., aheart rate) derived from the physiological data, a correlation lag valuederived from the physiological data, any other suitable value indicativeof a physiological rate or period thereof, or any combination thereof.In some embodiments, different processing modules may determine thevalue indicative of physiological rate and apply the bandpass filter, inwhich case the processing modules may communicate information to oneanother.

Step 6106 may include processing equipment determining a metric based onthe received physiological signal of step 6102. For example, theprocessing equipment may determine the metric in accordance with any ofthe techniques discussed in the context of FIGS. 11-41, any othersuitable techniques, or any combination thereof. The determined metricmay be indicative of de-trending, noise, any other value indicative ofthe physiological data, any other signal conditioning property derivedbased on the physiological data, or any combination thereof.

Step 6108 may include processing equipment selecting at least onebandpass filter setting based on the determined value indicative ofphysiological rate of step 6104 and based on the metric of step 6106.The at least one bandpass filter setting may include a center frequency,a bandwidth, a lower cutoff and an upper cutoff frequency, a shapeparameter, a type of bandpass filter, any other suitable parameteraffecting the spectral character of the filter (e.g., in units offrequency, angular frequency, wavenumber, period, or other suitableunits), any other suitable setting, or any combination thereof. Theselection of the bandpass filter may be based on a comparison of themetric of step 6106 to a threshold that may depend on the valueindicative of physiological rate.

Step 6110 may include processing equipment applying the bandpass filter,having the selected at least one setting of step 6108, to thephysiological data. The processing equipment may apply the bandpassfilter to the physiological data to generate a filtered signal. Theprocessing equipment may apply any suitable type of analog or digitalbandpass filter having, for example, any suitable passbandcharacteristics and any suitable roll-off characteristics. In someembodiments, the bandpass filter may be implemented as a combination ofa lowpass filter and a highpass filter, having suitable cutoffcharacteristics. In some embodiments, the bandpass filter may becentered about a frequency value corresponding to the physiologicalrate, and have a bandwidth determined based on the metric of step 6106.

In an illustrative example, the processing equipment may determine anoise metric based on the physiological data. The processing equipmentmay compare the noise metric to a threshold value. If the noise metricis determined to be below the threshold value (e.g., the signal isrelatively less noisy), which may be based on physiological rate, theprocessing equipment may increase the bandwidth of the bandpass filter,or cease bandpass filtering altogether to prevent the data componentscorresponding to desired physiological rate from being filtered out ofthe physiological data. For example, if the rate calculation isincorrectly calculating rate based on noise (e.g., because the bandpassfilter is being tuned to the frequency of the noise), this techniquewill enable the rate calculation to correct itself and lock onto thedesired rate. Conversely, if the noise metric is determined to exceed athreshold value (e.g., the signal is relatively more noisy), which maybe based on physiological rate, the processing equipment may decreasethe bandwidth of the bandpass filter, or begin bandpass filtering toreduce the noise components in the processed physiological data.

In some embodiments, processing equipment may perform a correlationcalculation using conditioned physiological data. The correlationcalculation may include selecting a template from the physiologicaldata, and correlating the template with the physiological data at asequence of lag values. The correlation may include an autocorrelation(e.g., the same set of data is correlated against itself), across-correlation with other data points or a reference (e.g., a set ofdata is correlated against another set of data or reference not sharingany data points), or a combination thereof. The correlation calculationmay provide several desirable benefits such as, for example, providing anormalized output, providing an indication of periodicity, providingrelatively sharper peaks than otherwise present in the physiologicaldata, and/or providing a metric of how much periodic character thephysiological data exhibits. Any of the techniques discussed in thecontext of FIGS. 62-85 may be applied, for example, at step 414 of flowdiagram 400 of FIG. 4.

FIG. 62 is a flow diagram of illustrative steps for performing acorrelation using a window of physiological data, in accordance withsome embodiments of the present disclosure. FIG. 64 is a diagram showingan illustrative window of physiological data and a template at severallags, in accordance with some embodiments of the present disclosure.FIG. 65 is a plot showing an illustrative correlation sequence for awindow of physiological data, in accordance with some embodiments of thepresent disclosure. FIGS. 64-65 will be referred to below during thediscussion of the illustrative steps of flow diagram 6200.

Step 6202 may include processing equipment receiving physiological datafrom a physiological sensor, memory, any other suitable source, or anycombination thereof. For example, referring to system 300 of FIG. 3, theprocessing equipment may receive a window of physiological data frominput signal generator 310. Sensor 318 of input signal generator 310 maybe coupled to a subject, and may detect physiological activity such as,for example, RED and/or IR light attenuation by tissue, using aphotodetector. In some embodiments, physiological signals generated byinput signal generator 310 may be stored in memory (e.g., RAM 54 of FIG.2, QSM 72 and/or other suitable memory) after being pre-processed bypre-processor 320. In such cases, step 6202 may include recalling datafrom the memory for further processing.

Step 6204 may include processing equipment selecting a portion of thephysiological data as a template. In some embodiments, the template mayinclude a predetermined number of samples of the physiological data. Forexample, referencing a six second window of data, the processingequipment may select the most recent three seconds of the data as thetemplate. In some embodiments, the template size may be constant (e.g.,a three second template). In some embodiments, the template size maydepend on a previously calculated rate (e.g., larger templates may beused for lower rates). In some embodiments, the template size may dependon one or more algorithm settings (e.g., the window of data and templatemay be larger while operating in an initialization mode or fast startmode).

Step 6206 may include processing equipment generating a correlationsequence based on the physiological data of step 6202 and template ofstep 6204. In some embodiments, the processing equipment may generatethe correlation sequence by multiplying the template values bycorresponding values of an equal size of the physiological data at aparticular lag. Step 6206 may include the processing equipmentnormalizing the physiological data of step 6202 and the selected portionof step 6204. In some embodiments, the processing equipment may use anysuitable signal conditioning technique on the physiological data (e.g.,de-trending and/or normalization techniques, scaling, shifting, or anyother suitable operation). For example, the processing equipment maynormalize the physiological data, the template, or both, to vary betweenzero and one, negative one and positive one, or any other predeterminedrange. In a further example, the processing equipment may first applythe techniques of any of signal conditioning techniques discussed in thecontext of FIGS. 42-61 at step 6206. In a further example, theprocessing equipment may normalize the physiological data, template, orboth, by dividing by the norm of the array of values to be normalized.In some embodiments, normalized data may have a mean value of zero(i.e., be centered about zero).

For example, referencing window of data of N points and a template ofthe most recent M data points, the processing equipment may use Eq. 36as shown below:

$\begin{matrix}{C_{j} = {\sum\limits_{i = 1}^{M}{S_{i}*X_{j,i}}}} & (36)\end{matrix}$to generate each value of the correlation sequence C_(j) for lag j, inwhich template S includes template values S_(i) for index i (whichranges from 1 to M), and X_(j,i) is the physiological data value atindex i, at a lag of j. For a template of the most recent M values ofthe physiological data, the set of values of the physiological data usedat any lag j may be determined from the set of N values of physiologicaldata as shown in Eq. 37:X _(j) =[X _((N−M)−j+1) ,X _((N−M)−j+2)) ,X _((N−M)−j+3) , . . . ,X_((N−M)−j+(M−1)) ,X _((N−M)−j+M)]  (37)which results in a set of M values of the physiological data. FIG. 64shows an illustrative window of data 6410, which is roughly six secondslong. A template 6420 is shown, corresponding to the most recent threeseconds of the physiological data. A correlation sequence may begenerated after the physiological data and template are normalized. Thephysiological data and the template may be, for example, normalized bysubtracting their respective mean values and dividing by theirrespective standard deviation values. Further, the correlation value maybe normalized based on the number of sample points in the template,resulting in a correlation value that varies between −1 and 1. As thelag increases from zero to (N−M), the template is correlated with valuesof the physiological data increasingly to the left, as indicated by thedirection of the arrows in FIG. 64. Note that the complete correlationsequence will include ((N−M)+1) values. FIG. 65 shows a plot 6500 ofillustrative correlation sequence 6502 derived from six seconds ofphysiological data using a three second template (at a sampling rate ofabout 57 Hz). The abscissa of plot 6500 is in units of point lag (e.g.,a lag of 10 corresponds to a shift of 10 points to the left, referencingFIG. 64). The physiological data and template used to generatecorrelation sequence 6502 were normalized, and accordingly correlationsequence 6502 was normalized to be bounded by negative one and positiveone.

FIG. 63 is a flow diagram 6300 of illustrative steps for generating acorrelation sequence using a window of physiological data, in accordancewith some embodiments of the present disclosure.

Step 6302 may include processing equipment receiving physiological datafrom a physiological sensor, memory, any other suitable source, or anycombination thereof. For example, referring to system 300 of FIG. 3, theprocessing equipment may receive a window of physiological data frominput signal generator 310. Sensor 318 of input signal generator 310 maybe coupled to a subject, and may detect physiological activity such as,for example, RED and/or IR light attenuation by tissue, using aphotodetector. In some embodiments, physiological signals generated byinput signal generator 310 may be stored in memory (e.g., RAM 54 of FIG.2, QSM 72 and/or other suitable memory) after being pre-processed bypre-processor 320. In such cases, step 6302 may include recalling datafrom the memory for further processing.

Step 6304 may include processing equipment selecting a portion of thephysiological data as a template. In some embodiments, the template mayinclude a predetermined number of samples of the physiological data. Forexample, referencing a six second window of data, the processingequipment may select the most recent three seconds of the data as thetemplate. In some embodiments, the template size may be constant (e.g.,a three second template). In some embodiments, the template size maydepend on a previously calculated rate (e.g., larger templates may beused for lower rates). In some embodiments, the template size may dependon one or more algorithm settings (e.g., the window of data and templatemay be larger while operating in an initialization mode or fast startmode).

Step 6306 may include the processing equipment normalizing thephysiological data of step 6302 and the selected portion of step 6304.In some embodiments, the processing equipment may use any suitablesignal conditioning technique (e.g., de-trending and/or normalizationtechniques, scaling, shifting, or any other suitable operation). Forexample, the processing equipment may normalize the physiological data,the template, or both, to vary between zero and one, negative one andpositive one, or any other predetermined range. In a further example,the processing equipment may apply the techniques of any of the signalconditioning techniques discussed in the context of FIGS. 42-61. In afurther example, the processing equipment may normalize thephysiological data, template, or both, by dividing by the norm of thearray of values to be normalized. In some embodiments, normalized datamay have a mean value of zero (i.e., be centered about zero).

Step 6308 may include processing equipment selecting a portion of thephysiological data based on a lag and the size of the template of step6304. Step 6310 may include normalizing the selected portion of data ofstep 6308 (e.g., using the same of step 6306 or a different technique).Step 6312 may include performing a correlation calculation using thetemplate and the selected portion of data of step 6308. Step 6314 mayinclude updating the lag value if appropriate. In some embodiments,steps 6308-6314 may be performed using a loop for a range of lag values.A first lag value may be used to perform step 6308 during the firstiteration, and the lag value may be updated (e.g., incremented) at step6314 for each subsequent iteration to some final value. For example, thefirst lag value may be zero, and the processing equipment may select thesame data points of the physiological data as the template to performthe correlation (e.g., giving a correlation value of one for suitablynormalized data and template). After performing the correlationcalculation, the processing equipment may increment the lag value by oneat step 6314 and repeat steps 6308-6312 to generate another point of thecorrelation sequence. In reference to FIG. 64, the final lag value maybe equal to (N−M), and the loop of flow diagram 6300 may be ceased whenthe processing equipment determines that the final lag value has beenreached (e.g., at step 6314). It will be understood that theillustrative correlation calculations discussed in the context of FIGS.62-65 are examples, and any suitable correlation calculation may beused, with a template of any suitable size and including any suitabledata points (e.g., the most recent data, or any other suitable data), togenerate a correlation sequence. It will also be understood that theprocessing equipment may calculate correlation sequence values for anysuitable set of one or more lag values. For example, in someembodiments, the processing equipment may only calculate correlationsequence values for lag values corresponding to relevant physiologicalrates (e.g., 20 to 300 BPM corresponding to lag values ranging from 3 to0.2 seconds, respectively)

FIG. 66 is a flow diagram 6600 of illustrative steps for identifying apeak of a correlation output greater than a threshold, in accordancewith some embodiments of the present disclosure. FIG. 69 is a plot 6900showing an illustrative correlation sequence 6902 for a window ofphysiological data, and several thresholds, in accordance with someembodiments of the present disclosure. FIG. 69 will be referred to belowduring the discussion of the illustrative steps of flow diagrams 6600and the illustrative steps of flow diagrams 6700 and 6800 of FIGS. 67and 68, respectively, which are discussed further below. A correlationoutput may include one or more peaks corresponding to relatively highcorrelation between two sets of data points. The illustrative steps of6600, 6700, and 6800 may be used to identify a particular peak,corresponding to a period (e.g., in lag value, time or sample number) ofa physiological rate.

Step 6602 may include processing equipment receiving correlation output(e.g., a correlation sequence). In some embodiments, the correlationoutput may have been generated using the illustrative techniques of flowdiagram 6200 of FIG. 62. In some embodiments, step 6602 may includerecalling the correlation output from memory. In some embodiments, thesame processing equipment (e.g., the same module or integrated circuit)may generate the correlation output and perform the steps of flowdiagram 6600, and accordingly, step 6602 need not be performed.

Step 6604 may include processing equipment identifying a peak in thecorrelation output of step 6602. In some embodiments, step 6604 mayinclude generating a threshold. The threshold may be generated using apredetermined value, a predetermined function, a value based on apreviously calculated rate, a value based on the current operating Mode,a value based one or more metrics derived from the physiological data(e.g., de-trending metrics, noise metrics), using any other suitabletechnique, or any combination thereof. The processing equipment mayidentify threshold crossings by comparing all or some of the correlationoutput to the threshold. The processing equipment may use any suitablepeak finding techniques to identify the peak such as, for example,identifying a maximum, identifying an upstroke (i.e., positive slope)and downstroke (i.e., negative slope), applying a threshold, comparingone or more peaks to identify a particular peak (e.g., a largest peak, apeak occurring first in terms of lag), any other suitable peak findingtechnique, or any combination thereof.

FIG. 67 is a flow diagram 6700 of further illustrative steps foridentifying a peak of a correlation output greater than a threshold, inaccordance with some embodiments of the present disclosure.

Step 6702 may include processing equipment receiving correlation output(e.g., a correlation sequence). In some embodiments, the correlationoutput may have been generated using the illustrative techniques of flowdiagram 6200 of FIG. 62. In some embodiments, step 6702 may includerecalling the correlation output from memory. In some embodiments, thesame processing equipment (e.g., the same module or integrated circuit)may generate the correlation output and perform the steps of flowdiagram 6700, and accordingly, step 6702 need not be performed.

Step 6704 may include processing equipment setting an indexcorresponding to a highest expected physiological rate. The processingequipment may use the index to indicate the data point of thecorrelation sequence under test. In some embodiments, the expected rangeof heart rates may range from 20 to 300 BPM, with the highest expectedrate being 300 BPM. At a correlation lag of zero, the correlationsequence value for normalized data and template may have a value of one.The next peak in the correlation may be expected roughly at a lag equalto the period associated with the physiological rate associated with thephysiological data. Accordingly, lags relatively smaller than the periodassociated with the highest expected rate need not be analyzed in somesuch circumstances. Step 6704 may include setting an index, in units oflag, to the minimum lag still expected to correspond to a physiologicalrate (e.g., the highest expected physiological rate), as shown by “X” instep 6704. For example, for a maximum expected rate of 300 BPM (i.e., 5Hz or a period of 0.2 seconds), and a sampling rate of 50 Hz (e.g., theinterval between lag points is 0.02 seconds), the minimum expected lagmay be about 0.2 seconds, or about 11 lag points (i.e., an index of 11if the zero lag point has index 1).

Step 6706 may include processing equipment determining whether thecorrelation sequence point corresponding to the current index is largerthan (or equal to) a threshold. In some embodiments, the processingequipment may directly compare the threshold value and the correlationsequence point. In some embodiments, the processing equipment maydetermine a difference, a ratio, any other suitable comparison metric,or any combination thereof, to determine the relative magnitudes of thecorrelation sequence point and the threshold.

In some embodiments, step 6706 may include generating the thresholdagainst which the correlation sequence point is compared. The thresholdmay be a constant value, a line, a polynomial of higher order than one,any other suitable value or function, or any combination thereof. Forexample, a sequence of threshold values T_(i) may be generated using Eq.38:T _(i) =K√{square root over (i)}  (38)in which index i corresponds to the lag value of the correlationsequence and K is a coefficient that may be constant, but need not be.In a further example, the sequence of threshold values may be generatedusing a shifted (along any suitable direction) square root function. Ina further example, the sequence of threshold values may be generatedusing a function that asymptotes or substantially trends toward a squareroot function. Plot 6900 of FIG. 69 shows three sets of threshold values6910, 6912, and 6914 generated using Eq. 38, having K values of about1.5/20, 1.3/20, and 1.1/20, respectively. In some embodiments, thethreshold type, or adjustable constants thereof, may be determineddepending on the operating Mode. For example, more stringent thresholds(e.g., larger threshold values) may be used in Mode 1, to ensure higherconfidence in the physiological rate before progressing to other Modes.In some embodiments, the processing equipment may search for twocrossings of the correlation sequence and threshold, corresponding to anupstroke and downstroke. In some embodiments, the processing equipmentmay only identify the first point (i.e., the point having the lowest lagvalue) that crosses the threshold. In some embodiments, the thresholdmay depend on whether the subject is a neonate, and accordingly maydepend on the properties of the physiological data.

If, at step 6706, the processing equipment determines that thecorrelation sequence point corresponding to the current index is largerthan the threshold, then the processing equipment may proceed to step6708. Step 6708 may include processing equipment determining whether asufficient peak is present. In some embodiments, the processingequipment may identify a peak based on the peak width. For example, theprocessing equipment may determine a full-width at half maximum (FWHM)value, or other suitable width metric to identify a peak as sufficient.In some embodiments, the processing equipment may require that a peakhave a particular number of points with positive slope before themaximum value, and a particular number of negative points with negativeslope after the maximum to be a peak. For example, the processingequipment may require that a peak have 4 points with positive slope and4 points with negative slope for a peak to be sufficient. In a furtherexample, referencing FIG. 69, the point determined to cross thethreshold may depend on the threshold value. For the three thresholds6910, 6912, and 6914, correlation sequence 6902 exhibits differentthreshold crossing points. The first peak, not including the peak at alag value of zero, is shown to cross thresholds 6912 and 6914, but notthreshold 6910. The second peak, not including the peak at a lag valueof zero, is shown to the cross all three thresholds 6910, 6912, and6914.

If, at step 6708, the processing equipment determines that a sufficientpeak is present, then the processing equipment may proceed to step 6710.Step 6710 may include processing equipment identifying the first peakqualified at step 6708 as a peak of the correlation output. In someembodiments, the processing equipment may identify one or more lagvalues associated with the peak. For example, the processing equipmentmay identify the maximum value of the first peak. If the processingequipment starts at the lowest relevant lag value (e.g., correspondingto the highest expected rate), once the first peak is identified theprocessing equipment need not search further, which may save computingresources. In some embodiments, the processing equipment may analyzesubsequent points to determine that the peak is sufficient, and need notanalyze any further points.

If, at either of steps 6706 or 6708, the processing equipment determinesthat a criteria has not been met at the current index (e.g., thecorrelation sequence value does not exceed the threshold, or the peak isnot sufficient), the processing equipment may then proceed to step 6712.Step 6712 may include the processing equipment determining whether toadvance the index to the next value. In some embodiments, there may bean upper limit on the index, and the processing equipment may determinethat the index is not to be advanced when the upper limit is reached.For example, an expected range of heart rates may range from 20 to 300BPM, with the lowest expected rate being 20 BPM. Accordingly, lagsrelatively larger than the period associated with the lowest expectedrate (e.g., 3 seconds for a rate of 20 BPM) need not be analyzed in somesuch circumstances, as shown by “Y” in step 6712. If the processingequipment determines that the index is not to be advanced at step 6712,then the processing equipment may proceed to step 6716, and determinethat no peak has been identified. If the processing equipment determinesthat the index is to be advanced at step 6712, then the processingequipment may proceed to step 6714 (e.g., increment the index), and thenperform at least one of steps 6706-6710 again.

FIG. 68 is a flow diagram 6800 of illustrative steps for identifying apeak of a correlation output as the correlation output is generated, inaccordance with some embodiments of the present disclosure. Flow diagram6800 is a modified version of flow diagram 6700 of FIG. 67, in whichcorrelation sequence values are analyzed as they are generated ratherthan after generation the entire set of correlation sequence values.Similarly numbered steps in flow diagram 6800 may operate similarly asdescribed in the discussion of flow diagram 6700. As shown in FIG. 69,the first threshold crossings, and corresponding identified peaks mayoccur at relatively small lag values, and the correlation sequence forhigher lag values, shown illustratively by region 6904, need not begenerated. The processing equipment may therefore save significantcomputing resources by generating only a portion of the correlationsequence.

Step 6720 of flow diagram 6800 may include processing equipmentdetermining a correlation based on the current index. Accordingly,points in the correlation sequence are generated one at a time, comparedagainst a threshold, and if a sufficient peak is present, a peak may beidentified without generating the entire correlation sequence. Theillustrative steps of flow diagram 6800 may use relatively lesscomputing resources than the illustrative steps of flow diagram 6700under some circumstances. In some embodiments, step 6720 may includeperforming any of the illustrative steps of, for example, flow diagrams6200 and 6300 of FIGS. 62 and 63, respectively.

FIG. 70 is a flow diagram 7000 of illustrative steps for performing acorrelation calculation using a correlation matrix, in accordance withsome embodiments of the present disclosure. The correlation matrixtechnique may use multiple templates selected from the physiologicaldata, and may be used to generate multiple correlation sequences. Thecorrelation matrix technique may be especially desirable when thephysiological data includes noisy portions, because at least some of thetemplates and resulting correlations may avoid the noisy portion. FIG.71 is a block diagram showing an illustrative window of physiologicaldata with generalized templates and lags, in accordance with someembodiments of the present disclosure. FIG. 72 is a diagram showing anillustrative lag matrix and correlation matrix, in accordance with someembodiments of the present disclosure. FIGS. 71-72 will be referred tobelow during the discussion of the illustrative steps of flow diagram7000.

Step 7002 may include processing equipment receiving physiological datafrom a physiological sensor, memory, any other suitable source, or anycombination thereof. For example, referring to system 300 of FIG. 3, theprocessing equipment may receive a window of physiological data frominput signal generator 310. Sensor 318 of input signal generator 310 maybe coupled to a subject, and may detect physiological activity such as,for example, RED and/or IR light attenuation by tissue, using aphotodetector. In some embodiments, physiological signals generated byinput signal generator 310 may be stored in memory (e.g., RAM 54 of FIG.2, QSM 72 and/or other suitable memory) after being pre-processed bypre-processor 320. In such cases, step 7002 may include recalling datafrom the memory for further processing.

Step 7004 may include processing equipment generating a lag matrix basedon the received physiological data of step 7002. The lag matrix mayinclude multiple sets of data points, each having the same length as adesired correlation template. In some embodiments, each row of the lagmatrix may correspond to a set of data points, offset by one sample fromadjacent rows. For example, FIG. 71 shows a window of physiological data7110, which has an illustrative length of about six seconds and N datapoints. FIG. 71 also shows illustrative template 7120, having a lengthof M data points, corresponding to data points ((N−k)−M)+1 through (N−k)of physiological data 7110, where k is an index of the number of datapoints that template 7120 is offset from the right end of physiologicaldata 7110. Accordingly, for lag j, Eq. 39 shown below:X _(j) =[X _(((N−k)−M)−j+1) ,X _(((N−k)−M)−j+2)) ,X _(((N−k)−M)−j+3) , .. . ,X _(((N−k)−M)−j+(M−1)) ,X _(((N−k)−M)−j+M)]  (39)may be used to determine the corresponding data points of physiologicaldata 7110 to be used in the correlation calculation. Note that Eq. 39may be used as a generalized version of Eq. 37, for a template selectedfrom any suitable portion of the physiological data (e.g., using indexk). The indices shown in FIG. 71 correspond to Eq. 39, although someindices have been simplified algebraically for convenience. The lagmatrix may include multiple collections of data points that correspondto the selected template at different lag values. For example,referencing FIG. 72, the processing equipment may apply, for example,Eq. 40:A _(rc) =X _((N−M)+c−(r−1))  (40)to generate lag matrix A, in which A_(rc) is the matrix value a row rand column C, N is the total number of data points, M is the templatesize, and X_((N−M)+c−(r−1)) is the sample value at the given index.Accordingly, lag matrix A has a size of (N−M)+1 rows by M columns. Eachrow of the lag matrix A may be a collection of M data pointscorresponding to a particular lag value. Each successive row has a lagof one more data point than the previous row, as shown by lag matrix7210 of FIG. 72. Note that the entries of lag matrix 7210 as shown inFIG. 72 are index values, while an actual lag matrix includes the samplevalues at the respective indices. The indices are presented for clarity,rather than the sample values themselves.

Step 7006 may include processing equipment generating a correlationmatrix based on the lag matrix of step 7004. In some embodiments, theprocessing equipment may generate a correlation matrix C using Eq. 41:C=A*A′  (41)in which A is the lag matrix and A′ is the transpose of the lag matrix.Accordingly, correlation matrix C may be square, and may have a size of(N−M)+1 rows by (N−M)+1 columns. In an illustrative example, the rows oflag matrix A may be considered collections of data corresponding to lagvalues j, and the columns of the transposed lag matrix A′ may beconsidered templates, each having a particular offset index k (e.g., asdescribed in FIG. 71). Accordingly, the values of the correlation matrixC_(k,j) for a particular offset k and lag j may be arranged as shown bycorrelation matrix 7230 of FIG. 72. The correlation matrix may includecorrelation values for each M point template (e.g., indexed by k) ateach lag value j. For example, each column of the correlation matrix mayrepresent a correlation sequence generated using a template of aparticular index k, for all lag values j. As shown by correlation matric7230, each successive column corresponds to a different template, at avalue of index k greater by one point than the template of the previouscolumn. Note that positive lag values correspond to the template shiftedleft from the zero lag value, and negative values correspond to thetemplate shifted right from the zero lag value. Alternatively, the rowsof lag matrix A may be considered templates, and the columns of thetransposed lag matrix A′ may be considered as collections of datacorresponding to lag values j, in which case the values of thecorrelation matrix may be arranged as C_(j,k). In some embodiments, theprocessing equipment may normalize the template, the corresponding datato be correlated with the template, and/or the correlation value itself,for each correlation calculation (i.e., each value in the correlationmatrix C).

Step 7008 may include processing equipment processing the correlationmatrix of step 7006 to generate processed correlation data. In someembodiments, using normalized templates and corresponding data, thevalues on the diagonal (e.g., row index=column index) of the squarecorrelation matrix C may be one, corresponding to a lag of zero (e.g.,see correlation matrix 7230 of FIG. 72 with j values of zero on thediagonal) for each template. In some such embodiments, step 7008 mayinclude applying a rotation operation to rotate the diagonal values 45°to be vertically or horizontally oriented in the processed correlationmatrix. The size of the rotated matrix may be larger than the originalcorrelation matrix. In some embodiments, processing the correlationmatrix may include averaging values of the correlation matrix along oneor more directions, therefore generating a one dimensional array valuesrather than a two dimensional matrix. For example, following a rotation,peak values in the processed correlation matrix may substantially alignalong a row or column, and the values along the row or column may beaveraged, depending on the rotation. In a further example, theprocessing equipment may average values of a correlation matrix along adirection of fixed lag value.

Step 7010 may include processing equipment identifying one or more peaksof the processed correlation data of step 7008. In some embodiments,step 7010 may include generating the threshold. The threshold may begenerated using a predetermined value, a predetermined function, usingany other suitable technique, or any combination thereof. The processingequipment may identify threshold crossings by comparing all or some ofthe correlation output to the threshold. The processing equipment mayuse any suitable peak finding techniques to identify the peak such as,for example, identifying a maximum, identifying an upstroke (i.e.,positive slope) and downstroke (i.e., negative slope), applying athreshold, comparing one or more peaks to identify a particular peak(e.g., a largest peak, a peak occurring first in terms of lag value),any other suitable peak finding technique, or any combination thereof.

FIG. 73 is a plot 7300 showing a graphical representation 7302 of anillustrative correlation matrix, in accordance with some embodiments ofthe present disclosure. Graphical representation 7302 of the correlationmatrix includes a series of peaks normal to the coordinate axes (i.e.,into and out of the page), indicated by the shaded diagonal regions.Graphical representation 7302 of the correlation matrix has a primarydirection shown by arrow 7304 along which the lag is zero, or a multipleof the period associated with the physiological rate. FIG. 74 is a plot7400 showing graphical representation 7302 of the illustrativecorrelation matrix of FIG. 73 facing a primary direction (i.e., thedirection of arrow 7304, directed into the page in FIG. 74) to show therange of correlation matrix values, in accordance with some embodimentsof the present disclosure. The relatively largest peak shown in FIG. 74corresponds to a lag value of zero, with additional peaks on either sidecorresponding to multiples of the period associated with thephysiological rate. In some embodiments, the processing equipment mayapply a matrix operation such as, for example, a 45° CW rotation to acorrelation matrix, so that a primary direction substantially alignsalong a row or a column. The units of plot 7400 are not the same as theunits of plot 7300 due to the direction and orientation of view. In someembodiments, the processing equipment may use one or more properties ofthe correlation matrix to aid in identifying a peak, determining a lagvalue, or both. For example, using a processed correlation matrixgenerated by a 45° CW rotation and averaging of the columns (e.g., togive a one-dimensional array), the processing equipment may use a lagvalue of zero as a reference, as shown by dashed line 7402 in FIG. 74.From the reference, which is associated with a maximum peak, theprocessing equipment may analyze lag values incrementally outward ineach direction to identify a first peak. The processing equipment mayuse threshold 7404, which may be similar to the thresholds of plot 6900,referenced to the zero lag position and extending symmetrically in eachdirection, to identify the first peak. The processing equipment mayaccordingly determine the lag value associated with the peak, in whichthe lag value may be indicative of a period associated with aphysiological rate. In some embodiments, the processing equipment mayidentify a peak in each direction and average the associated lag values.In some embodiments, the processing equipment may identify a peak ineach direction and identify the peak as the peak having the lesserassociated lag value.

As discussed above, a correlation calculation provides a singlecorrelation value for each lag value between two segments ofphysiological data (e.g., a template and corresponding portion of dataat a lag value). The segments of physiological data may include arelatively large amount of information, which is not necessarily fullyrepresented by the single correlation value. In some embodiments,statistical regression analysis (SRA) may be used to, for example, aidin performing a correlation calculation, modify a correlationcalculation, identify a peak in a correlation sequence, or a combinationthereof, by analyzing information additional to the correlation value ofthe two segments of physiological data. For example, SRA may be used todetermine a metric based on the two segments, and the metric may be usedto weight the correlation values of a correlation calculation using thetwo segments. In a further example, SRA may be used to determine ametric, which may be used to identify a peak in a correlation output.The SRA techniques disclosed herein may be especially useful when, forexample, applied to lag values correspond to peaks that just exceed orjust do not exceed a threshold (e.g., are very near a threshold), wherepeak identification may be sensitive to variations in data and moreinformation on the correlation may be desired. FIGS. 75-85 are includedas illustrative examples, although it will be understood that theillustrative SRA techniques disclosed herein do not necessarily requiregenerating any plots.

FIG. 75 is a panel of three plots 7500, 7510, and 7520, respectivelyshowing an illustrative window of data 7502, and two sets of twosegments of physiological data having a relative lag, in accordance withsome embodiments of the present disclosure. The abscissa of plots 7500,7510, and 7520 is shown in units of sample number (at a sampling grateof about 57 Hz), while the ordinates are shown in arbitrary units.Illustrative window of data 7502, including about 342 data points,exhibits six full peaks of de-trended physiological data. Thephysiological rate associated with window of data 7502 (and segments7512, 7514, 7522 and 7524 thereof) is near 1 Hz (e.g., having a periodof about 1 second), with some variation. Physiological data 7502 isroughly periodic, having a series of peaks and troughs spaces by aperiod corresponding to a physiological rate (e.g., the period is thereciprocal of the rate in suitable units). A correlation calculation ofa segment of data 7502 with another segment of data 7502 at a lag ofzero or an integer multiple of the period will give a relatively largevalue (e.g., a correlation coefficient of near one, or “correlated”)because the peaks and troughs of one segment will substantially line upwith the respective peaks and troughs of the other segment.Alternatively, a correlation calculation of a segment of data 7502 withanother segment of data 7502 at a lag of a half period or a half periodplus an integer multiple of the period will give a relatively largenegative value (e.g., a correlation coefficient of near negative one, or“anti-correlated”) because the peaks and troughs of one segment willsubstantially line up with the respective troughs and peaks of the othersegment. Lags values between half and full periods are expected toresult in intermediate correlation values. Plot 7510 shows segment 7512and segment 7514, each including about 171 points, having a relative lagof 28 samples (e.g., about a half period). Plot 7520 shows segment 7522and segment 7524, each including about 171 points, having a relative lagof 55 samples (e.g., about a full period). Segments 7512 and 7514 resultin a relatively large negative correlation value (e.g., a negativecorrelation value for de-trended segments), while segments 7522 and 7524result in relatively high correlation (e.g., a positive correlationvalue for de-trended segments). In some embodiments, SRA may be used toextract further information than just a correlation value between twosegments. For example, correlation calculations performed at twodifferent lag values may result in similar correlation values, but thedata may of the data segments may line up more closely in at one of thelag values, indicating the likely true period. It will be understoodthat any suitably sized window of data, and segment thereof, may be usedin accordance with the present disclosure, and that a window size of 342samples is used for illustration. It will also be understood that anysuitable sampling rate may be used in accordance with the presentdisclosure, and that roughly 57 Hz is used for illustration.

In some embodiments, a first and second segment of physiological datamay be paired to generate a set of value pairs, thus reducing the twosegments to a single set of value pairs that may be analyzed. Each valuepair may include a value of the first segment, and a corresponding valueof the second segment (e.g., graphically, this can be represented bypoints in two-dimensional space, where each axis represents values froma respective segment). Using such a construct in a graphical example,value pairs corresponding to highly correlated segments will tendtowards a line of slope one, through the origin (e.g., the line y=x).Using the same construct in this graphical example, value pairscorresponding to highly anti-correlated segments will tend towards aline of slope negative one, through the origin (e.g., the line y=−x).Further, using the same construct in a graphical example, value pairscorresponding to non-correlated segments would be expected to berandomly distributed in the two-dimensional plane about the origin(e.g., in a Gaussian distribution). FIG. 76 is a panel of twoillustrative plots 7600 and 7610 each showing a set of a template andcorresponding data of FIG. 75 plotted against each other, in accordancewith some embodiments of the present disclosure. Plot 7600 shows valuepairs 7602 corresponding to segments 7512 and 7514, in which for eachsample value shown in plot 7510, the values of segments 7512 and 7514are combined as a coordinate pair in plot 7600. The value pairs 7602 maybe generated using Eq. 42, as shown below:P _(i)=(S _(1,i) ,S _(2,i))  (42)in which P_(i) is a value of value pairs 7602 for index i, and S_(j,i)are the values of segments 7512 and 7514 of FIG. 75 (either order may beused) for index j (e.g., ranging from 1 to 2 corresponding to a firstand second segment in either order). Plot 7610 shows a value pairs 7612corresponding to segments 7522 and 7524 of FIG. 75, in which for eachsample value shown in plot 7520, the values of segments 7522 and 7524are combined as a coordinate pair in plot 7610 (e.g., using Eq. 42).Value pairs 7602 are indicative of the relatively high anti-correlationof segments 7512 and 7514 of FIG. 75, exhibited by the relatively largedeviation of value pairs 7602 from line 7604 having unit slope andpassing through the origin of plot 7600 (e.g., the slope of the trendline of value pairs 7602 is much closer to −1). Value pairs 7612 areindicative of the relatively high correlation of segments 7522 and 7524of FIG. 75, exhibited by the relative agreement of value pairs 7612 withline 7614 having unit slope and passing through the origin of plot 7610.While a correlation value may provide a single representative value ofeach of sets of points 7602 and 7612, additional information isavailable if desired, as discussed below in the context of FIG. 77, forexample. For example, correlation values calculated at two different lagvalues may have similar correlation values which may indicate a peak,but their distribution of value pairs (e.g., generated using Eq. 42) maydiffer. A lag value truly corresponding to the period of a physiologicalrate is expected to result in value pairs that line up well with a linesuch as line 7614. A lag value that does not correspond to the period ofa physiological rate is expected to result in value pairs that do notline up well with a line such as line 7614. Accordingly, SRA may aid indiscerning a true peak from an artifact peak in a correlationcalculation based on physiological data.

The distribution of value pairs in plots 7600 and 7610 of FIG. 76 alsoprovide insight into how well the original segments are correlated.Segments of physiological data centered about zero exhibiting peaks andtroughs will typically have larger slopes near value so of zero andsmall slopes near the maximum and minimum values (e.g., the slope at thezenith of a peak or the bottom of a trough is substantially zero).Accordingly, upstrokes and downstrokes typically include fewer datapoints, while there are relatively more data points near the maximumsand minimums (e.g., at the peaks and troughs). Value pairs 7602 showrelatively large groupings of points in the upper left and lower rightcorners, substantially corresponding to pairings of peak values withtrough values from the two segments. Alternatively, value pairs 7612show relatively large groupings of points in the lower left and upperright corners, substantially corresponding to pairings of peak valueswith peak values, and trough values with trough values from the twosegments. The groupings of value pairs 7602 and 7612 are oriented indifferent directions, essentially normal to one another. While ahorizontal or vertical distribution of value pairs 7602 and 7612 mayrespectively appear similar, for example, a 45° rotation of the valuepairs aligns the point groupings in sometimes more convenient directions(e.g., horizontal or vertical). Flow diagrams 7700, 7900, 8100, and 8200of FIGS. 77, 79, 81, and 82 discussed below provide some description oftechniques for analyzing distributions of value pairs.

FIG. 77 is a flow diagram 7700 of illustrative steps for determining ametric from two segments of physiological data, and using the metric tomodify correlation output or identify a correlation peak, in accordancewith some embodiments of the present disclosure. In some embodiments,the processing equipment may generate a correlation sequence based onthe first and second segments using a sequence of lag values. Theillustrative techniques of flow diagram 6600 may be used to determinefurther information regarding the two segments.

Step 7702 may include processing equipment selecting a first segmentfrom a window of physiological data. In some embodiments, the firstsegment may have a predetermined length in time or number of samples.For example, referencing FIG. 75, segment 7514 includes about 171samples of roughly 342 samples of a full window of data. In someembodiments, the length of the first segment may depend on a previouslycalculated rate (e.g., relatively longer segments may be used forrelatively lower rates).

Step 7704 may include processing equipment selecting a second segmentfrom the window of physiological data, shifted in time (e.g., lag insample number) relative to the first segment of step 7702. In someembodiments, the second segment may have the same length as the firstsegment (e.g., the same total number of samples). In some embodiments,the lag may be determined based on a previously calculated rate. Forexample, the processing equipment may select a lag equal to the periodassociated with a previously calculated rate. In some embodiments,multiple lags may be selected, and accordingly, multiple second segmentsmay be selected, creating multiple pair of the first segment and secondsegments.

Step 7706 may include processing equipment analyzing the first andsecond segments of steps 7702 and 7704. In some embodiments, step 7706may include determining a correlation value between the first and secondsegments. In some embodiments, step 7706 may include generating newvalue pairs from the first and second segments using, for example, Eq.42.

Step 7708 may include processing equipment determining a metric based onthe analysis of step 7706. In some embodiments, the metric may indicatehow well the first and second segments are correlated. In someembodiments, the metric may indicate a comparison between new valuepairs generated from the first and second segments and reference valuepairs (e.g., a reference distribution or other function). The metric maybe normalized to range from zero to one, or scaled to any other suitablerange. In some embodiments, the metric may be based on a statisticalcalculation.

Step 7710 may include processing equipment modifying a correlationoutput, identifying a peak in a correlation output, or both, based onthe metric of step 7708. In some embodiments, the processing equipmentmay use the metric of step 7708 to weight one or more points of acorrelation sequence between the first and second segments. For example,if the determined metric indicates poor correlation between the firstand second segments, then the processing equipment may down-weight thecorresponding point of a correlation sequence between the first andsecond segments. In some embodiments, the processing equipment mayimplement the illustrative techniques of flow diagram 7700 to identify apeak in a correlation sequence. For example, the illustrative techniquesof flow diagram 7700 may be used with step 6604 of flow diagram 6600 ofFIG. 66 or step 6706 of flow diagrams 6700-6800 of FIGS. 67-68.

FIG. 78 is a panel of two plots corresponding to the plots of FIG. 76after an illustrative transformation of the data, in accordance withsome embodiments of the present disclosure. The illustratedtransformation in FIG. 78 includes a 45° clockwise (CW) rotation of thevalue pairs, orienting lines 7604 and 7614 of FIG. 76 horizontal inplots 7800 and 7810, respectively. It will be understood that thistransformation is illustrated for clarity, and that any suitabletransformation, or no transformation, may be performed on value pairsgenerated from a first and second segment. The techniques discussedbelow in the context of FIGS. 79-85 may be applied to untransformed ortransformed data. In some circumstances, the 45° CW rotation maysimplify further calculations because the correlation axis is rotated tohorizontal, allowing convenient partitioning of horizontal and verticalproperties of a set of data points. The 45° CW rotation may be generatedfor a value pair, for example, using Eq. 43:

$\begin{matrix}{\begin{bmatrix}x_{i,r} \\y_{i,r}\end{bmatrix} = {\begin{bmatrix}{\cos\;\theta} & {{- \sin}\;\theta} \\{\sin\;\theta} & {\cos\;\theta}\end{bmatrix}\begin{bmatrix}x_{i} \\y_{i}\end{bmatrix}}} & (43)\end{matrix}$in which original points (x_(i), y_(i)) are rotated by an angle θ of−45°, resulting in corresponding rotated points (X_(i,r), y_(i,r)).Value pairs 7802 of plot 7800 were generated by performing the 45° CWrotation of value pairs 7602 of FIG. 76. Value pairs 7812 of plot 7810were generated similarly from value pairs 7612 of FIG. 76. In someembodiments, the data used to generate value pairs 7802 and 7812 mayhave undergone a mean subtraction and normalization based on thestandard deviation of the data. In each of plots 7800 and 7810, pointsbetween −0.5 and 0.5 are not filled, points between 0.5/−0.5 and 1/−1are hatched, and points outside of −1 and 1 are filled black. Referenceslines 7806 and 7808 indicate the ordinate value of one, while referencelines 7816 and 7818 indicate the ordinate value of negative one. Valuepairs 7802 exhibit relatively more points outside of −1 and 1 (i.e.,filled black in FIG. 78) than value pairs 7812, indicating relativelymore points of value pairs 7802 deviating from the line 7804 than pointsof value pairs 7812 deviating from the line 7814. Accordingly, one ormore metrics may be determined which may quantify such differences,which may indicate how well the original segments are correlated.

FIG. 79 is a flow diagram 7900 of illustrative steps for determining ametric from a vertical distribution, and using the metric to modifycorrelation output or identify a correlation peak, in accordance withsome embodiments of the present disclosure. In some embodiments,referencing plots 7800 and 7810 of FIG. 78, analysis of the verticaldistribution of rotated value pairs (e.g., of a matrix following a 45°CW rotation) may provide correlation information. For example, valuepairs 7802 of plot 7800 have a relatively broad vertical distribution ascompared to value pairs 7812 of plot 7810. Further, value pairs 7802 ofplot 7800 have a double-peaked vertical distribution as compared tovalue pairs 7812 of plot 7810 which exhibit substantially a single peak.For correlated segments, the vertical distribution is expected toexhibit substantially a single peak near zero, while the verticaldistribution for anti-correlated segments is expected to exhibit twopeaks spaced on both sides of zero. It will be understood the valuepairs need not be rotated, and that the illustrative techniquesdescribed below may be applied in any suitable direction. Rotation ofthe value pairs by −45° merely provides a convenient illustrativeexample.

Step 7902 may include processing equipment selecting a first segmentfrom a window of physiological data. In some embodiments, the firstsegment may have a predetermined length in time or number of samples.For example, referencing FIG. 75, segment 7514 includes about 171samples of the roughly 342 samples of a full window of data. In someembodiments, the length of the first segment may depend on a previouslycalculated rate (e.g., relatively longer segments may be used forrelatively lower rates).

Step 7904 may include processing equipment selecting a second segmentfrom the window of physiological data, shifted in time (e.g., lag insample number) relative to the first segment of step 7902. In someembodiments, the second segment may have the same length as the firstsegment (e.g., the same total number of samples). In some embodiments,the lag may be determined based on a previously calculated rate. Forexample, the processing equipment may select a lag equal to the periodassociated with a previously calculated rate. In some embodiments,multiple lags may be selected, and accordingly, multiple second segmentsmay be selected, creating multiple pair of the first segment and secondsegments.

Step 7906 may include processing equipment generating a matrix based onthe first and second segments of steps 7902 and 7904. In someembodiments, the matrix may be generated using, for example, Eq. 42 togenerate a set of value pairs (e.g., a 2×N matrix of N value pairs)based on the segments. In some embodiments, a transform may beoptionally performed on the matrix (e.g., mean subtraction,normalization, rotation). For example, the value pairs may be rotated by−45° using Eq. 43. In such examples following the rotation, referencinga geometric interpretation, the value pairs may each include ahorizontal value (e.g., the “x” value” in typical Cartesiancoordinates), and a vertical value (e.g., the “y” value” in typicalCartesian coordinates). It will be understood that horizontal andvertical are referenced to rotated value pairs, although any suitabledirectional axes may be used as references. For example, referencingun-rotated value pairs, the directional axes given by “y=x” and “y=−x”may be used as references.

Step 7908 may include processing equipment analyzing the verticaldistribution of points in the matrix of step 7906. In some embodiments,step 7908 may include processing equipment analyzing points in adirection substantially perpendicular to a correlation axis (e.g.,perpendicular to lines 7604 and 7614 of FIG. 76, and lines 7804 and 7814of FIG. 78). For example, referencing FIG. 76, step 7908 may includeanalyzing the distribution of value pairs 7602 relative to line 7604,although the data points need not be plotted to perform step 7908. In afurther example, referencing FIG. 78, step 7908 may include analyzingthe distribution of value pairs 7802 relative to line 7804, although thedata points need not be plotted to perform step 7908. As discussedabove, segments having a lag value of an integer multiple of the periodassociated with the physiological rate are expected to have verticaldistributions exhibiting a single peak at zero. For example, highlycorrelated segments (i.e., a correlation coefficient of near one) wouldall lie on the horizontal axis after a −45° rotation, and therefore thevertical distribution would resemble a Delta function (e.g., thedistribution is nearly single valued, with the instance of all othervalues being substantially zero). The Delta function is sharper than aGaussian, with a much smaller spread, and serves as a theoretical limitin this example, although physiological data will have some variationand will likely not achieve a Delta function.

Step 7910 may include processing equipment determining a metric base onthe analysis of step 7908. In some embodiments, the metric may beindicative of the shape in the vertical distribution. For example, themetric may be indicative of the distribution peak, spread, or both. Insome embodiments, the metric may be a value normalized between zero andone. In some embodiments, the analysis may include determining a numberof value pairs having a vertical value within two threshold values(e.g., between −1 and 1, between −0.5 and 0.5, between −1 and 1excluding points between −0.5 and 0.5, or any other suitable thresholdrange). In some embodiments, the ratio of value pairs having a verticalvalue in a particular threshold range to the total number of pairs maybe used as a metric. For example, the number of value pairs having avertical value outside of −1 and 1 may be divided by the total number ofvalue pairs to give a metric. In some embodiments, the analysis mayinclude determining a distribution of vertical values of the valuepairs. For example, the processing equipment may generate a histogram ofthe vertical values of the value pairs. In a further example, theprocessing equipment may generate a cumulative distribution (e.g., anintegral or sum of the histogram), and analyze the cumulativedistribution of vertical values.

In some embodiments, the analysis may include a normality test or otherstatistical test. In some embodiments, the analysis may includegenerating a reference distribution (a histogram or cumulativedistribution derived thereof), and comparing the distribution ofvertical values with the reference distribution. For example, theprocessing equipment may determine a mean and standard deviation of thevertical values, generate a normal distribution with the same standarddeviation and mean, and determine a difference (e.g., a sum of squareddifferences between each value and the corresponding value of the normaldistribution) between the distribution of vertical values and the normaldistribution. In a further example, the processing equipment maydetermine a skewness value, kurtosis value, or both, based on thevertical values and compare this value(s) with a threshold or referencevalues. In a further example, the processing equipment may perform aJarque-Bera (JB) Test using, for example, Eq. 44:

$\begin{matrix}{M = {\frac{N}{6}\left( {S^{2} + \frac{\left( {K - 3} \right)^{2}}{4}} \right)}} & (44)\end{matrix}$in which test metric M is based on number of points N, sample skewnessS, and sample kurtosis K. The test metric M is zero for a normaldistribution and increase as the vertical values deviate from a normaldistribution. In some embodiments, for example, the test metric M may becompared to a threshold to determine whether to modify a correlationvalue, or scaled and used as a confidence value directly to modify acorrelation value. In a further example, the processing equipment mayperform a Kolmogorov-Smirnoff test such as, for example, a LillieforsTest based on the cumulative distribution of vertical values, with themetric being the maximum discrepancy, a sum of differences, or any othersuitable value indicative of the difference. In some embodiments, forexample, the value indicative of the difference may be compared to athreshold to determine whether to modify a correlation value, or scaledand used as a confidence value directly to modify a correlation value.FIG. 81 is a flow diagram 8100 of illustrative steps for analyzing avertical distribution, in accordance with some embodiments of thepresent disclosure. In some embodiments, the analysis of step 7908 offlow diagram 7900 may include comparing the distribution of points inthe vertical direction with a reference distribution. For example, insome embodiments, the processing equipment may compare the verticaldistribution of points (e.g., generated using a histogram) with a normaldistribution, as shown by step 8102. The processing equipment may, forexample, determine the square root of the sum of squared differencesbetween the distribution of vertical values and the referencedistribution, and use the difference as a confidence metric (e.g., wherelarger differences correspond to reduced confidence in correlationbetween the segments). In a further example, in some embodiments, theprocessing equipment may perform a Jarque-Bera test (e.g., using Eq. 44)on statistical metrics derived from the vertical distribution of points,as shown by step 8104. The processing equipment may, for example,determine the Jarque-Bera metric, and use the difference as a confidencemetric (e.g., where larger differences correspond to reduced confidencein correlation between the segments). In a further example, in someembodiments, the processing equipment may perform a Lilliefors test onthe vertical distribution of points, or a cumulative distributionderived thereof, as shown by step 8104. The processing equipment may,for example, determine the maximum difference between the cumulativedistribution of vertical values and a reference cumulative distribution,and use the difference as a confidence metric (e.g., where a largerdifference correspond to reduced confidence in correlation between thesegments).

Step 7912 may include processing equipment modifying a correlationoutput, identifying a peak in a correlation output, or both, based onthe metric of step 7910. In some embodiments, the processing equipmentmay use the metric of step 7910 to weight one or more points of acorrelation sequence between the first and second segments. For example,if the determined metrics indicates poor correlation between the firstand second segments, then the processing equipment may down-weight thecorresponding point of a correlation sequence between the first andsecond segments. In some embodiments, the processing equipment mayimplement the illustrative techniques of flow diagram 7900 to identify apeak in a correlation sequence. For example, the illustrative techniquesof flow diagram 7900 may be used with step 6604 of flow diagram 6600 ofFIG. 66 or step 6706 of flow diagrams 6700-6800 of FIGS. 67-68.

FIG. 80 is a panel of the two plots 7800 and 7810 of FIG. 78 and tworespective plots of corresponding vertical distributions, in accordancewith some embodiments of the present disclosure. Plot 8000 showsvertical distribution 8002 corresponding to value pairs 7802, along withGaussian profile 8004 for reference. Plot 8010 shows verticaldistribution 8012 corresponding to value pairs 7812, along with Gaussianprofile 8014 for reference. Vertical distributions 8002 and 8012 aregenerated from histograms of respective value pairs 7802 and 7812,normalized to an area under the curve of one. Gaussian profiles 8004 and8014 are also scaled to an area under the curve of one, for reference.Vertical distribution 8002 exhibits two primary peaks, indicative of therelatively large number of value pairs 7802 outside of the −1 to 1threshold (i.e., points filled black in plot 7800 of FIG. 80). Verticaldistribution 8012 exhibits a single primary peaks, indicative of therelatively large number of value pairs 7812 near zero. Accordingly,comparison of a vertical distribution with a reference profile mayindicate information about the correlation calculation. For example, therelatively high correlation associated with value pairs 7812 maycorrespond to a single primary peak in vertical distribution 8012. Whilethe distribution of vertical values of value pairs 7812 do not exactlyfollow Gaussian profile 8014, they do exhibit a central peak. Thedistribution of vertical values of value pairs 7802 follow Gaussianprofile 8004 even less closely, and exhibit two peaks rather than one.Accordingly, the metric discussed in the context of step 7910 of flowdiagram 7900 of FIG. 79 may indicate that the distribution of verticalvalues of value pairs 7812 are relatively closer to a Gaussian profile,and the corresponding segments of physiological data are likelycorrelated. Also accordingly, the metric discussed in the context ofstep 7910 of flow diagram 7900 of FIG. 79 may indicate that thedistribution of vertical values of value pairs 7802 are relativelydifferent from a Gaussian profile, and the corresponding segments ofphysiological data are likely anti-correlated. The coarseness of thehistogram generated from the vertical values may impact the shape of thehistogram. For example, in some circumstances, as the histogram “bins”are made coarser, tall peaks in the distribution may be smoothedlaterally. In an illustrative example, the relatively large peak invertical distribution 8012 may be smoothed laterally with coarserhistogram bins, thus lessening the difference between verticaldistribution 8012 and Gaussian profile 8014. Any suitable level ofcoarseness may be used in generating a histogram and correspondingcumulative distribution, and determining a difference between adistribution and a reference distribution.

FIG. 82 is a flow diagram 8200 of illustrative steps for determining ametric from a horizontal distribution, and using the metric to modifycorrelation output or identify a correlation peak, in accordance withsome embodiments of the present disclosure. For correlated segments, thehorizontal distribution is expected to exhibit two peaks on either sideof zero, while the horizontal distribution for anti-correlated segmentsis expected to exhibit a single peak near zero. This is due to thegrouping of value pairs and their orientation along a particulardirection, as discussed above in the context of FIG. 76. Value pairsgenerated from correlated segments will include peak-peak value pairsand trough-trough value pairs, with value pairs including smaller valuesbeing less numerous due to the high slopes of the data at those regions.Accordingly, after a −45° rotation the groupings substantially lie nearthe horizontal axis, on opposite sides of zero resulting in asubstantially two peaked distribution in the horizontal values. Valuepairs generated from anti-correlated segments will include peak-troughvalue pairs and trough-peak value pairs, with value pairs includingsmaller values being less numerous due to the high slopes of the data atthose regions. Accordingly, after a −45° rotation the groupingssubstantially lie near the vertical axis, on opposite sides of zeroresulting in a substantially single peaked distribution in thehorizontal values. It will be understood the value pairs need not berotated, and that the illustrative techniques described below may beapplied in any suitable direction. Rotation of the value pairs by −45°merely provides a convenient illustrative example.

Step 8202 may include processing equipment selecting a first segmentfrom a window of physiological data. In some embodiments, the firstsegment may have a predetermined length in time or number of samples. Insome embodiments, the length of the first segment may depend on apreviously calculated rate (e.g., relatively longer segments may be usedfor relatively smaller rates).

Step 8204 may include processing equipment selecting a second segmentfrom the window of physiological data, shifted in time relative (e.g.,lag in sample number) to the first segment of step 8202. In someembodiments, the second segment may have the same length as the firstsegment (e.g., the same total number of samples). In some embodiments,the lag may be determined based on a previously calculated rate. Forexample, the processing equipment may select a lag equal to the periodassociated with a previously calculated rate. In some embodiments,multiple lags may be selected, and accordingly, multiple second segmentsmay be selected, creating multiple pair of the first segment and secondsegments.

Step 8206 may include processing equipment generating a matrix based onthe first and second segments of steps 8202 and 8204. In someembodiments, the matrix may be generated using, for example, Eq. 42 togenerate a set of coordinate pairs (e.g., a 2×N matrix of N coordinatepairs) based on the segments. In some embodiments, a transform may beoptionally performed on the matrix (e.g., mean subtraction,normalization, rotation). For example, a 45° CW rotation may beperformed on the matrix using Eq. 43, for example, which may simplifysubsequent calculations.

Step 8208 may include processing equipment analyzing the horizontaldistribution of points in the matrix of step 8206. In some embodiments,step 8208 may include processing equipment analyzing points in adirection substantially parallel to a correlation axis (e.g., parallelto lines 7604 and 7614 of FIG. 76, and lines 7804 and 7814 of FIG. 78).For example, referencing FIG. 76, step 8208 may include analyzing thedistribution of value pairs 7602 along line 7604, although the datapoints need not be plotted to perform step 8208. In a further example,referencing FIG. 78, step 8208 may include analyzing the distribution ofvalue pairs 7802 along line 7804, although the data points need not beplotted to perform step 8208. Step 8210 may include processing equipmentdetermining a metric base on the analysis of step 8208.

Step 8212 may include processing equipment modifying a correlationoutput, identifying a peak in a correlation output, or both, based onthe metric of step 8210. In some embodiments, the processing equipmentmay use the metric of step 8210 to weight one or more points of acorrelation sequence between the first and second segments. For example,if the determined metric indicates poor correlation, or anti-correlationbetween the first and second segments, then the processing equipment maydown-weight the corresponding point of a correlation sequence betweenthe first and second segments. In a further example, if the determinedmetric indicates good correlation (e.g., the existence of two peaks inthe distribution of horizontal values) between the first and secondsegments, then the processing equipment may down-weight thecorresponding point of a correlation sequence between the first andsecond segments. In some embodiments, the processing equipment mayimplement the illustrative techniques of flow diagram 8200 to identify apeak in a correlation sequence. For example, the illustrative techniquesof flow diagram 8200 may be used with step 6604 of flow diagram 6600 ofFIG. 66 or step 6706 of flow diagrams 6700-6800 of FIGS. 67-68.

For example, FIG. 83 is a panel of the two plots of FIG. 78 and tworespective plots 8300 and 8310 of corresponding horizontaldistributions, in accordance with some embodiments of the presentdisclosure. Referencing plots 8300 and 7800, horizontal values 8302 arethe horizontal distribution derived from value pairs 7802. Horizontalvalues 8304 are the horizontal distribution derived by generating acoordinate pair using Eq. 42, using segment 7512 of FIG. 75 as bothsegments (i.e., a lag of zero), and then rotating the value pairs CW45°. As shown in plot 8300, rotated value pairs generated fromcorrelated segments (in this example, at a lag of zero) exhibit twopeaks, while rotated value pairs from substantially anti-correlatedsegments exhibit a single peak centered at zero. Referencing plots 8310and 7810, horizontal distribution 8312 is the horizontal distributionderived from value pairs 7812. Horizontal distribution 8314 is generatedbased on Eq. 42, using segment 7522 of FIG. 75 as both segments (i.e., alag of zero), and then rotating the value pairs CW 45°. As shown in plot8310, rotated value pairs generated from well correlated segments suchas segments spaced by a lag value of zero, or a substantially integermultiple of the period associated with the physiological rate, exhibittwo peaks in their horizontal distribution.

FIG. 84 is a panel of the two plots of illustrative cumulativedistributions of horizontal values, in accordance with some embodimentsof the present disclosure. Plot 8400 shows cumulative distributionfunctions (CDF) 8402, 8404, and 8406. Plot 8410 shows cumulativedistribution functions (CDF) 8412, 8414, and 8416. CDFs 8406 and 8416each correspond to original physiological data (e.g., paired with itselfas both the first and second segment with a lag of zero), and exhibit abimodal shape (e.g., as seen by the two regions of relatively higherslope). This shape of the CDF indicates that the histogram of horizontalvalues exhibits two peaks, indicative of good correlation. CDFs 8402 and8404 shown in plot 8400 each correspond to transformed data at aparticular lag value, which is likely not indicative of a periodassociated with a physiological rate. In some embodiments, CDFs 8402 and8404 may correspond to a subset of the transformed data. For example,when the transformed data is centered at zero, CDF 8402 may correspondto the data within plus and minus one half of a standard deviation andCDF 8404 may correspond to the data within plus and minus one standarddeviation. In some circumstances, using a subset of the transformed datamay provide a better cumulative distribution. CDFs 8402 and 8404 exhibita shape indicative of histograms of horizontal values exhibiting asingle peak, which is not indicative of good correlation between thecorresponding segments. CDFs 8412 and 8414 of plot 8410 each correspondto transformed data at a particular lag value, which is likelyindicative of a physiological rate. CDFs 8412 and 8414 exhibit a shapeindicative of histograms of horizontal values exhibiting two peaks,which is indicative of good correlation between the correspondingsegments. In some embodiments, CDFs 8412 and 8414 may correspond to asubset of the transformed data. For example, when the transformed datais centered at zero, CDF 8412 may correspond to the data within plus andminus one half of a standard deviation and CDF 8414 may correspond tothe data within plus and minus one standard deviation. Accordingly, if aCDF such as either of CDFs 8406 and 8416 corresponding to originalphysiological data is used as a reference CDF, the processing equipmentmay determine a difference metric between the reference CDF and thecumulative distribution of horizontal values at the lag value other thanzero. The processing equipment may use the difference value to qualifyor disqualify a peak, modify a correlation value (e.g., decrease a valueif the difference is large, and increase the correlation value if thedifference is small), determine a confidence value, or a combinationthereof.

FIG. 85 is a flow diagram 8500 of illustrative steps for applyingstatistical regression analysis, in accordance with some embodiments ofthe present disclosure.

Step 8502 may include processing equipment receiving physiological data,or conditioned data derived thereof, from a physiological sensor,memory, any other suitable source, or any combination thereof. In someembodiments, physiological signals generated by input signal generator310 of FIG. 3 may be stored in memory (e.g., RAM 54 of FIG. 2, QSM 72and/or other suitable memory), conditioned, or both, after beingpre-processed by pre-processor 320 of FIG. 3. In such cases, step 8502may include recalling data from the memory for further processing.

Step 8504 may include processing equipment generating a correlationsequence based on segments of the physiological data of step 8502. Thecorrelation sequence may include a sequence of correlation valuescorresponding to different lag values. In some embodiments, theprocessing equipment may generate the correlation sequence bymultiplying and summing values of a first segment of the physiologicalsegment with corresponding values of a second segment of thephysiological data, shifted in time by a particular lag, for multiplelag values. Step 8504 may include the processing equipment normalizingthe physiological data, or segments thereof, of step 8502. In someembodiments, the processing equipment may use any suitable signalconditioning technique (e.g., de-trending and/or normalizationtechniques, scaling, shifting, or any other suitable operation). Forexample, the processing equipment may normalize segments ofphysiological data to vary between zero and one, negative one andpositive one, or any other predetermined range. In an illustrativeexample, the processing equipment may receive physiological data at step8502, select a segment of the data (e.g., the most recent data of apredetermined size), and generate a correlation sequence for all lagvalues between the segment and second segments of the physiological data(e.g., lag values ranging from zero to N where N is the number ofsamples of the segment) at step 8504.

Step 8506 may include processing equipment identifying a peak in thecorrelation sequence. In some embodiments, the processing equipment mayuse a threshold to determine if a peak is sufficient. If the correlationsequence includes one or more points exceeding the threshold, theprocessing equipment may proceed to step 8508. The threshold may includeany suitable value or values such as, for example, a fixed value, afunction, a piece-wise function, or other set of values. If thecorrelation sequence does not include one or more points exceeding thethreshold, the processing equipment may determine that no valid lag wasidentified, in which case the processing equipment may generate a newcorrelation sequence with more recent data, repeating steps 8502-8506.In some embodiments, the processing equipment may identify a value ofthe correlation sequence that exceeds the threshold. In someembodiments, the processing equipment may identify a peak by identifyinga maximum value in the correlation sequence, and determining that apredetermined number of correlation sequence values at adjacent smallerlags have positive slope (e.g., an upstroke), and that a predeterminednumber of correlation sequence values at adjacent larger lags havenegative slope (e.g., a downstroke).

Step 8508 may include processing equipment determining one or moremetrics based on the physiological data. In some embodiments, step 8508may include the processing equipment performing one or more SRAtechniques, such as those described in the context of FIGS. 77, 79, 81and 82, to determine the one or more metrics. For example, theprocessing equipment may perform a Lilliefors Test, a Jarque-Bera (JB)Test, a Kolmogorov Smirnov (KS) Test, any other suitable test, or anycombination thereof, to determine the one or more metrics. For example,for each of the lag value corresponding to an identified peak, apredetermined number of previous lag values, and a predetermined numberof subsequent lag values, the processing equipment may generate a matrixof value pairs between the segment and respective second segment andperform a 45° CW rotation of the data (e.g., as described in the contextof flow diagram 7700 of FIG. 77). The processing equipment may thennormalize the data accordingly. The processing equipment may thencalculate one or more of a Lilliefors metric, a JB metric, a KS metric,and the fraction of the residual within an amount (e.g., one half) of astandard deviation of the transformed data (e.g., the number of valuepairs between references lines 7806 and 7808 in plot 7800 of FIG. 83divided by the total number of value pairs in plot 7800). The processingequipment may combine the metrics for the lag values (e.g., identifiedlag, previous lags, and subsequent lags).

Step 8510 may include processing equipment evaluating the one or moremetrics determined at step 8508. In some embodiments, the processingequipment may perform one or more tests at step 8508, and evaluate theoutcome of the one or more tests at step 8510. For example, theprocessing equipment may perform a Jarque-Bera Test at step 8508, andthen evaluate the JB metric against a look-up table to determine whetherthe JB Test has passed. In a further example, the processing equipmentmay perform a Lilliefors Test, a Jarque-Bera Test, a Kolmogorov SmirnovTest, and determine a metric value at step 8508, and then evaluate theresults of the four tests at step 8510. For example, if the LillieforsTest, JB Test, and KS Test are determined to have been passed, theprocessing equipment may accordingly increase the correlation valuecorresponding to the identified lag value relative to the remainingvalues of the correlation sequence. If only some of the Tests aredetermined to have been passed, the processing equipment may maintainthe correlation value corresponding to the identified lag value. If noneof the Tests are determined to have been passed, the processingequipment may decrease the correlation value corresponding to theidentified lag value. In some embodiments, the processing equipment maycompare the correlation value corresponding to the identified lag valueto a threshold, and if the correlation value exceeds the threshold, theprocessing equipment may then evaluate the test results (e.g., metrics)from step 8508.

Step 8512 may include processing equipment qualifying or disqualifying apeak based on the evaluation of step 8510. In some embodiments, if theprocessing equipment evaluates the results of or more tests at step8510, and determines that the one or more tests has passed, theprocessing equipment may qualify an identified peak. In someembodiments, if the processing equipment evaluates the results of ormore tests at step 8510, and determines that the one or more tests havenot passed, the processing equipment may disqualify an identified peak.In some embodiments, the processing equipment may modify a correlationvalue based on the one or more evaluated metrics. For example, anevaluated metric may include a Jarque-Bera metric which is used toup-weight or down-weight the corresponding correlation value. In someembodiments, the processing equipment may modify one or more values ofthe correlation sequence based on the evaluated one or more metrics, andqualify or disqualify a peak based on the modified correlation sequence.

In some embodiments, the processing equipment may perform aqualification of one or more correlation lag values (e.g., as shown bystep 418 of flow diagram 400 of FIG. 4). Qualification of the determinedone or more correlation lag values may provide a more robust operation,and reduce the misidentification of noise activity as a physiologicalrate. For example, Qualification Techniques may be used to detectinstances in which the system has locked on to one half, double, orother multiple of the actual physiological rate. Qualification mayprovide an indication of how consistent the lag value is, and how wellthe lag value characterizes the period of a physiological rate.

FIG. 86 is a flow diagram 8600 of illustrative steps for qualifying ordisqualifying value that may be indicative of a physiological rate usinga cross-correlation, in accordance with some embodiments of the presentdisclosure.

Step 8602 may include processing equipment receiving qualificationinformation. Qualification information may include a correlation lagvalue, a qualification metric, any other suitable information used inqualifying a correlation lag value, or any combination thereof. In someembodiments the qualification information may be generated at an earliertime, and stored in suitable memory (e.g., RAM 54, ROM 52, or othermemory of physiological monitoring system 10 of FIGS. 1-2). Accordingly,in some embodiments, step 8602 may include recalling the qualificationinformation from memory. In some embodiments, a single processor,module, or system may determine, store or otherwise process thequalification information and perform steps 8604-8608, and accordingly,step 8602 need not be performed.

Step 8604 may include the processing equipment scaling one or morepredefined templates based on the qualification information. Thepredefined templates may include asymmetrical pulses without a dicroticnotch, asymmetrical pulses with a dicrotic notch, approximatelysymmetrical pulses without a dicrotic notch, pulses of any otherclassification, any other suitable type of template, or any combinationthereof. In some embodiments, the predefined templates may be derivedfrom a subject's PPG signals. In some embodiments, the predefinedtemplates may be mathematical functions, mathematical approximations toa PPG signal, any other suitable mathematical formulation, or anycombination thereof. In some embodiments, scaling a predefined templatemay include stretching or compressing the template in the time domain(or corresponding sample number domain) to match a characteristic timescale of the template to that associated with the qualificationinformation. For example, if an correlation lag value corresponds to aphysiological rate of 1 Hz, a predefined template may be scaled tocorrespond to 1 Hz. The period associated with the lag will be referredto herein as “P”, and may be used for qualification. Scaled templatesmay be referred to herein as “reference waveforms.”

Step 8606 may include the processing equipment performing across-correlation of the scaled template(s) of step 8604 and thephysiological signal. The physiological signal may be conditioned beforebeing used in the cross-correlation. The signal conditioning may includeremoving DC components, low frequency components, or both, from thesignal. In some embodiments, a varying baseline may be removed from thesignal (i.e., de-trending) to constrain the data average to zero. Thesignal may also be normalized so that the amplitude of the signal isone. The cross-correlation may be performed using any suitablealgorithm. The output of step 8606 may be a cross-correlation output,which may include a series of cross-correlation values taken for acorresponding series of time lags (or sample number lags) between thepredefined template and the physiological signal.

Step 8608 may include the processing equipment analyzing thecross-correlation output(s) of step 8606 to qualify or disqualify thecorrelation lag value. A conditioned cross-correlation output of step8606 may include one or more peaks, indicative of high correlationbetween the physiological signal and a predefined template. In someembodiments, step 8608 may include analyzing one or more peaks. In someembodiments, step 8608 may include analyzing one or more segments of thecross-correlation output. For example, tests described herein below suchas the Symmetry Test, Radius Test, Angle Test, Area Test, AreaSimilarity Test, and Statistical Property Test, or any other suitabletest, or combination thereof, may be performed at step 8608 to analyzethe one or more cross-correlation outputs of step 8606. The processingequipment may perform any suitable analysis at step 8608 to qualify ordisqualify lag values associated with the one or more cross-correlationoutputs. In some embodiments, when a value is disqualified, the valuemay still be used although a rate filter may be modified. For example, afilter weight associated with the disqualified value may be reduced as aresult of disqualification.

FIG. 87 is a plot 8700 of an illustrative PPG signal 8702 showing onepulse of a subject, in accordance with some embodiments of the presentdisclosure. The abscissa of plot 8700 is presented in arbitrary units,while the ordinate is also presented in arbitrary units. PPG signal 8702exhibits non-zero baseline 8704, which may be, but need not be, astraight line. While PPG signal 8702 is relatively free of noise, slopedbaseline 8704 indicates a relatively low frequency noise component. Insome embodiments, PPG signal 8702 may be a raw PPG signal, a smoothedPPG signal, an ensemble averaged PPG signal, a filtered PPG signal, anyother raw or processed PPG signal, or any combination thereof. In someembodiments, PPG signal 8702 may be approximated by a mathematicalrepresentation for further processing (e.g., step 8606, step 8608, orboth, of flow diagram 8600).

FIG. 88 is a plot 8800 of a template 8802 derived from illustrative PPGsignal 8702 of FIG. 87 with baseline 8704 removed (i.e., de-trended), inaccordance with some embodiments of the present disclosure. The abscissaof plot 8800 is presented in arbitrary units, while the ordinate is alsopresented in arbitrary units. The conditioned signal derived from PPGsignal 8702 is referred to as template 8802. De-trending, or othersuitable conditioning, may be performed as part of step 8604 of flowdiagram 8600. Although the ordinate of FIG. 88 is not numerated,template 8802 may be normalized or otherwise scaled along the ordinateof plot 8800 to range from [−1,0], [−1,1], [0,1], or any other suitablerange. For example, the mean of template 8802 may be set to zero.

FIG. 89 is a plot 8900 of illustrative template 8802 of FIG. 88 scaledto different sizes for use as templates in performingcross-correlations, in accordance with some embodiments of the presentdisclosure. The abscissa of plot 8900 is presented in arbitrary units,while the ordinate is also presented in arbitrary units. Illustrativescaled templates 8904, 8906, and 8908 are generated by scaling template8802 by respective scale factors. In the illustrated example, with theabscissa beginning at zero, the pulse period is scaled by one-half,one-fourth, and one-eighth, to generate scaled templates 8904, 8906, and8908, respectively.

The plots of FIGS. 87-89 illustrate one technique for creating templatesfor use in qualifying a correlation lag value. The starting pulse inFIG. 87 may be selected from the subject being monitored or may beselected offline from a library of stored PPG signals from subjects. Insome embodiments, the starting pulse may be generated mathematically(e.g., based on one or more functions) or manually. In some embodiments,the processing equipment may generate the one or more templates for eachqualification calculation. In some embodiments, a library of templatesfor one or more pulse shapes may be pre-generated and/or pre-stored inmemory and the processing equipment may select the appropriate templateor templates for use in the qualification based on the receivedqualification information. For example, 281 templates may be stored foreach pulse shape, one for each BPM in the range of 20-300 BPM. This ismerely illustrative and any suitable number of templates may be storedof any suitable BPM resolution and covering any suitable range of BPMs.The processing equipment may select an appropriate template based onrate information in the qualification information. In some embodiments,the pulse shape of the templates may vary as a function of BPM. Forexample, at low BPM values (e.g., less than about 60 BPM) the pulseshape may be asymmetrical with a dicrotic notch. However, as the BPMvalue increases, the pulse shape may become more symmetrical and thedicrotic notch may become relatively smaller and eventually not includedin the pulse shape.

FIG. 90 is a plot 9000 of output 9002 of an illustrativecross-correlation between a PPG signal or a signal derived thereof and apredefined template, in accordance with some embodiments of the presentdisclosure. The abscissa of plot 9000 is presented in units ofcross-correlation lag, while the ordinate is presented in arbitraryunits, with zero notated. The shape of cross-correlation output 9002indicates a relatively close match between the template and the PPGsignal. In some embodiments, more than one cross-correlation output maybe generated by using more than one scaled template. Accordingly, insome embodiments, cross-correlation outputs corresponding to multipletemplates may be evaluated (e.g., using any of the cross-correlationanalyses of the present disclosure) to determine which template mostclosely matches the PPG signal.

FIG. 91 is a flow diagram 9100 of illustrative steps for qualifying ordisqualifying a value that may be indicative of a physiological ratebased on an analysis of two segments of a cross-correlation, inaccordance with some embodiments of the present disclosure. Theillustrative steps of flow diagram 7900 may be referred to as the“Symmetry Test.”

Step 9102 may include processing equipment receiving a cross-correlationsignal (e.g., generated according to step 8606 of FIG. 86) as an input.In some embodiments, a cross-correlation signal may be across-correlation output (e.g., output of step 8606 of flow diagram 8600of FIG. 86). In some embodiments, the cross-correlation signal may begenerated by a cross-correlation module. In some embodiments thecross-correlation signal may be generated at an earlier time, and storedin suitable memory. Accordingly, in some embodiments, step 9102 mayinclude recalling the stored cross-correlation signal from the memory.In some embodiments, a single processor, module, or system may performthe cross-correlation and steps 9104-9108, and accordingly, step 9102need not be performed.

Step 9104 may include the processing equipment selecting any twosuitable segments, having any suitable length (e.g., one full period, ormore, or less), of the cross-correlation signal of step 9102. Periodhere refers to a period associated with a correlation lag value. Thecross-correlation signal may include a series of data points exhibitingpeaks and valleys. The two segments may be selected on either side of apeak or valley, in symmetric locations (e.g., see FIGS. 92-93 for a moredetailed discussion of the segments' symmetry). For example, thesegments may share a single point at the zenith of a peak or the nadirof a valley, and the segments may extend in opposite directions. In afurther example, the segments need not share any points, and a gap mayexist between the segments.

FIG. 92 is a plot 9200 of an illustrative cross-correlation signal 9202,showing several reference points for selecting two segments andgenerating a symmetry curve, in accordance with some embodiments of step9104 of the present disclosure. As shown by Eqs. 45 and 46:u _(j) =f(x ₀ +Δx _(j))  (45)v _(j) =f(x ₀₀ −Δx _(j))  (46)the reference points may be used to generate a series of points (u_(j),v_(j)) in a Cartesian plane (or other suitable coordinate system),termed a “symmetry curve.” Reference points x₀ and x₀₀ may be the samepoint located at a peak or valley (i.e., x₀=x₀₀ as shown by point 9204or point 9214), or points x₀ and x₀₀ may be different points, forexample, symmetrical about a peak (or valley) of cross-correlationsignal values f(x) (e.g., as shown by points 9206 and 9208, or points9210 and 9212). Note that x may include discrete values associated witha sampled signal. Shift Δx_(j) may range from zero to any suitablenumber, generating two segments (i.e., the first and second segments, orvice versa) with data points at (x₀+Δx_(j)) and (x₀₀−Δx_(j)) for asuitable span of index j values. If the cross-correlation signal issymmetric (about the midpoint of x₀ and x₀₀), as shift Δx_(j) increases,u_(j) and v_(j) will be equal numerically. The series of points (u_(j),v_(j)) will accordingly generate a straight line of unity slope throughthe origin. Any deviation between u_(j) and v_(j) may be attributed toasymmetry of the cross-correlation output, and will accordingly causethe series of points (u_(j), v_(j)) to not fall in a straight line ofunity slope through the origin.

Step 9104 may include the processing equipment selecting any twosuitable segments, of any suitable length (e.g., one full period, ormore, or less), of a cross-correlation output (e.g., cross-correlationsignal 9202). For example, the portion of cross-correlation signal 9202between points 9208 and 9210 may be a first segment, with the secondsegment extending leftward from point 9206 to point 9220. In a furtherexample, the portion of cross-correlation signal 9202 between points9208 and 9210 may be a first segment, with a second segment extendingrightward from point 9212 to point 9218. In a further example, theportion of cross-correlation signal 9202 between points 9204 and 9214may be a first segment, and a second segment may extend from point 9214to points 9216.

Step 9106 may include the processing equipment analyzing the twosegments of step 9104. In some embodiments, step 9106 may includeanalyzing the symmetry of the two segments. For example, step 9106 mayinclude the processing equipment generating a symmetry curve to analyzethe symmetry between the first and second segments. FIG. 93 is a plot9300 of an illustrative symmetry curve 9302 generated using two segmentsof a cross-correlation signal, in accordance with some embodiments ofthe present disclosure. The demarcations of the abscissa and ordinate ofplot 9300 are scaled in arbitrary, but similar, units. Symmetry curve9302 may be generated, for example, by tracing out a series of points(u_(j), v_(j)) in a Cartesian plane (or other suitable coordinatesystem), as shown by Eqs. 45 and 46. Symmetry curve 9302 is observed topass roughly through the origin at (0,0) and have a slope of roughlyone, with some relatively small deviation. Line 9304 shows the linethrough the origin with a slope of unity, for reference. In somecircumstances, symmetry curve 9302 may be determined to be shapedsufficiently well (e.g., based on the analysis of step 9106 of flowdiagram 9100), and a correlation lag value may be qualified.

In some embodiments, step 9106 may include comparing a symmetry curve toa reference curve (e.g., a line through the origin with a slope ofunity). For example, a variability metric such as V₁ may be computedaccording to Eq. 47:

$\begin{matrix}{V_{1} = {\sum\limits_{i = 1}^{M}\left( {{S\left( x_{i} \right)} - {y\left( x_{i} \right)}} \right)^{2}}} & (47)\end{matrix}$in which S(x_(i)) is a symmetry curve value (e.g., a value of symmetrycurve 9302) at point i of M data points, and y(x_(i)) corresponds to areference curve (e.g., line 9304 in which y(x_(i))=x_(i)). Any suitablevariability metric may be used to evaluate the symmetry curve, orotherwise the symmetry of the two segments.

In some embodiments, step 9106 may include comparing the two segmentswithout first generating a symmetry curve or using a reference curve.For example, a variability metric such as V₂ may be computed accordingto Eq. 48:

$\begin{matrix}{V_{2} = {\sum\limits_{j = 1}^{M}{\left( {u_{j} - v_{j}} \right)^{2}.}}} & (48)\end{matrix}$As a further example, a correlation coefficient between the two segmentsmay be computed. Any suitable correlation coefficient computation may beused including, for example, Pearson's correlation coefficient.

In some embodiments, multiple pairs of first and second segments may beselected at step 9104 and analyzed at step 9106. For example, a firstpair of segments sharing a single common point may each extend for afull pulse period, while a second pair of segments sharing the samecommon point may extend for a half pulse period. In a further example,different pairs of segments may be selected each referenced to differentpeaks or valleys of the cross-correlation signal.

Step 9108 may include the processing equipment qualifying, ordisqualifying, one or more correlation lag values based on the analysisof step 9106. In some embodiments, one or more metric may be inputtedinto a classifier (e.g., a neural network). In some embodiments,qualification (or disqualification) may depend on a comparison betweenone or metrics computed at step 9106 and one or more threshold values.For example, a variability metric (e.g., V₁ of Eq. 47 or any othersuitable metric) may be compared with a threshold, and if thevariability metric does not exceed the threshold value, the associatedcorrelation lag value may be qualified. Accordingly, if the variabilitymetric exceeds the threshold value, the associated correlation lag valuemay be disqualified (and vice versa). In some embodiments, adisqualification at step 9108 may trigger steps 9104-9108 to repeat(i.e., two different segments may be selected from the samecross-correlation signal for analysis). In some embodiments, adisqualification at step 9108 may trigger steps 9102-9108 to repeat(i.e., two segments are selected from a different cross-correlationsignal for analysis). In some embodiments, a disqualification at step9108 may trigger an analysis different than that of flow diagram 9100 tobe performed to either qualify or disqualify the correlation lag valueassociated with the cross-correlation signal.

FIG. 94 is a flow diagram 9400 of illustrative steps for qualifying ordisqualifying one or more values that may be indicative of aphysiological rate based on an analysis of offset segments of across-correlation signal, in accordance with some embodiments of thepresent disclosure. In some embodiments, the illustrative steps of flowdiagram 9400 aid in preventing a double-rate/half-period condition, orother condition in which period P is not indicative of a physiologicalrate.

Step 9402 may include processing equipment receiving a cross-correlationsignal (e.g., generated according to step 8606 of FIG. 86) as an input.In some embodiments, the cross-correlation signal may be generated by across-correlation module. In some embodiments the cross-correlationsignal may be generated at an earlier time, and stored in suitablememory. Accordingly, in some embodiments, step 9402 may includerecalling the stored cross-correlation signal from the memory. In someembodiments, a single processor, module, or system may perform thecross-correlation and steps 9404-9418, and accordingly, step 9402 neednot be performed.

Step 9404 may include the processing equipment selecting two segments ofthe cross-correlation signal of step 9402. The cross-correlation signalmay include a series of data points exhibiting peaks and valleys. Thetwo segments may be of equal length, offset by a quarter length of theperiod P associated with the correlation lag value. In some embodiments,the first and second segments may each have a length equal to period P,although they may be any suitable length as long as the segments are ofapproximately equal length. The two segments may be selected from anysuitable portion of the cross-correlation signal. Note that the twosegments will be referred to as Segment1 and Segment2, or the firstsegment and the second segment, although the designations arearbitrarily chosen for illustration purposes (e.g., the first and secondsegments may be interchanged in accordance with the present disclosure).

Following step 9404, the processing equipment may perform step 9406,9412, or both (e.g., simultaneously or sequentially) in accordance withsome embodiments of the present disclosure. In some embodiments, the“Radius Test” (i.e., steps 9406-9410) may be performed, and the “AngleTest” (i.e., steps 9412-9416) need not be performed. In someembodiments, the “Angle Test” may be performed, and the “Radius Test”need not be performed. In some embodiments, one of the “Radius Test” andthe “Angle Test” may be performed initially, and depending upon theoutcome of the test (e.g., qualify, disqualify) the other test may beperformed.

step 9406 may include the processing equipment calculating the radiusbased on the two segments of step 9404, using Eq. 49. In Eq. 49,Segment1_(j) and Segment2_(j) are the cross-correlation output f(x)values of the first and second segments, respectively, evaluated forindex j, which ranges from zero to N−1 for segments with N data points.Note that x may include discrete values associated with a sampledsignal. With reference to Eqs. 50 and 51, Segment1 originates at pointx₀ and extends rightward with index j in the Cartesian plane, and theorigin of Segment2 is offset by a quarter period P relative to the firstsegment. Also note that Δx_(j) may increase linearly with j. Forexample, Δx_(j) may be given by Eq. 52, in which Δx is the data pointspacing in the domain.

$\begin{matrix}{{Radius}_{j} = \sqrt{{{Segment}1}_{j}^{2} + {{Segment}\; 2_{j}^{2}}}} & (49) \\{{{Segment}\; 1_{j}} = {f\left( {x_{0} + {\Delta\; x_{j}}} \right)}} & (50) \\{{{Segment}\; 2_{j}} = {f\left( {x_{0} + \frac{P}{4} + {\Delta\; x_{j}}} \right)}} & (51) \\{{\Delta\; x_{j}} = {j\;\Delta\; x}} & (52)\end{matrix}$

The radius of step 9406, as computed using Eq. 49, will be a constant ifthe period P identically matches the period of the cross-correlationoutput f(x), and has strictly sinusoidal character. Variations in theradius with index j may be attributed to the shape of thecross-correlation output, deviations between period P and thecharacteristic period of the cross-correlation signal, any othersuitable characteristic of the cross-correlation signal, or anycombination thereof.

Step 9408 may include the processing equipment identifying the maximumand minimum radii from step 9406, and calculating a ratio as shown, forexample, by Eq. 53.

$\begin{matrix}{{Ratio} = \frac{{Radius}_{MAX}}{{Radius}_{MIN}}} & (53)\end{matrix}$The ratio of the maximum (Radius_(MAX)) and minimum (Radius_(MIN)) radiiprovides a variability metric, indicating variability in the calculatedradii values. Note that if the radii values have low variability (i.e.,are all substantially the same value), the ratio is near one. Thepresence of variability will necessarily cause the ratio to assume avalue greater than one. In some embodiments, variability metrics otherthan the ratio of Eq. 53 may be calculated from the radii values. Anysuitable variability metric may be used in accordance with the presentdisclosure.

Step 9410 may include the processing equipment comparing the ratio ofstep 9408 to one or more threshold values. In some embodiments, a fixedthreshold may be used for comparison. In some embodiments, a variablethreshold may be used. For example, threshold value(s) may be based onthe value of the period P, subject information (e.g., subject history,medical history, medical procedure), segment length, any other suitableinformation, or any combination thereof. In some embodiments, thethreshold value(s) may be stricter in an initial mode versus asubsequent mode. The stricter threshold(s) may prevent the algorithmfrom locking onto the wrong rate. Steps 9412-9416 may be performed inconcert with, or in lieu of, steps 9406-9410, in accordance with someembodiments of the present disclosure.

Step 9412 may include the processing equipment calculating the angle foreach index j of the two segments of step 9404, using Eq. 54. Each anglevalue, per index j, is the arctangent of the ratio of the first andsecond segment values, given by Eqs. 50 and 51, respectively.

$\begin{matrix}{{Angle}_{j} = {\tan^{- 1}\left( \frac{{Segment}\; 2_{j}}{{Segment}\; 1_{j}} \right)}} & (54)\end{matrix}$

Step 9414 may include the processing equipment calculating the sum ofthe differences between each adjacent angle of step 9412, also referredto as “phase unwrapping”, as shown by Eq. 55:

$\begin{matrix}{{Sum}_{angle} = {\sum\limits_{j = 1}^{N - 1}{\left( {{Angle}_{j} - {Angle}_{j - 1}} \right).}}} & (55)\end{matrix}$The sum may be approximately proportional to the length of the segments.Segments of length equal to period P should give a sum of roughly 360°,the period P is approximately equal to the characteristic period of thecross-correlation signal. If the cross-correlation exhibits a periodhalf that of period P, then the sum may tend towards 720° for segmentlengths equal to period P. The sum will depend, however, on the lengthof the segments. For example, longer segments (in terms of period P)tend to provide larger sums.

Step 9416 may include the processing equipment comparing the sum of step9414 to one or more threshold values, or range thereof. In someembodiments, the one or more thresholds may include an upper and lowerlimit. In some embodiments, a threshold value may be based on thesegment length. For example, a sum may be compared to a threshold rangeof 300-420°, for segments having a length equal to period P. In afurther example, larger threshold values may be used with segmentshaving a length longer than period P. In some embodiments, for a givensegment length, a threshold value may vary. For example, thresholdvalue(s) may be based on the value of the period P, subject information(e.g., subject history, medical history, medical procedure), whethercorrelation lag values are being qualified, any other suitableinformation, or any combination thereof. In some embodiments, thethreshold value(s) may be stricter in a particular Mode (e.g.,Initialization Mode) versus another Mode (e.g., using a bandpassfilter).

Step 9418 may include the processing equipment qualifying ordisqualifying the associated correlation lag value based on thecomparison of step 9410, the comparison of step 9416, or both. If theratio, angle sum, or both, exceed the respective threshold value, thecorrelation lag value may be disqualified. If the ratio, angle sum, orboth, do not exceed the threshold value, the correlation lag value maybe qualified.

In some embodiments, the processing equipment may generate a metricbased on a history of angle sum values stored in a buffer (e.g., anglesum values calculated using Eq. 55 for the previous 12 seconds, or anyother suitable time interval). For example, the processing equipment maydetermine a metric M using an expression such as Eq. 56:M=√{square root over ((SUM_(max)−SUM_(min)))}(SUM_(mean))^(2−2*SUM)^(mean)   (56)where SUM_(max) is the maximum angle sum value stored in the buffer,SUM_(min) is the minimum angle sum value stored in the buffer, andSUM_(mean) is the mean angle sum value stored in the buffer. All of theSUM values shown in Eq. 56 are normalized by subtraction 360°, and thendividing by 360° to generate a non-dimensional angle sum variable. Insome embodiments, the buffer may store normalized angle sum values innon-dimensional form. This metric analyzes the variability of the anglesum metric over time. In some situations, the angle metric may indicatea good angle even though the correlation lag value does not correspondto the physiological rate. However, in these situations, the anglemetric may vary over time. Accordingly, metric M may be determined andcompared to one more thresholds to qualify or disqualify a correlationlag value. Metric M may be used in addition to or in place of the AngleTest. It will be understood that Eq. 56 is merely illustrative and anysuitable variation metric may be used. In some embodiments, theprocessing equipment may apply a variability metric such as anexpression similar to Eq. 56 to any other suitable noise metric,de-trending metric, qualification metric, or other metric, to determinea variation over time of the metric.

FIG. 95 shows four sets of plots, arranged by row, of (from left toright) illustrative cross-correlation signals and corresponding symmetrycurves, radial curves, radius calculations (top), and angle calculations(bottom), in accordance with some embodiments of the present disclosure.

Plots 9500, 9510, 9520, and 9530 show four respective, illustrativecross-correlation signals, in accordance with some embodiments of thepresent disclosure. The top two cross-correlation signals, of plots 9500and 9510, do not exhibit notches and show relatively consistent periodiccharacter. Plots 9502 and 9512 each show a symmetry curve, with lengthsequal to 1.6 times period P, which corresponds to the cross-correlationsignal of plots 9500 and 9510, respectively. The symmetry curves ofplots 9502 and 9512 show substantially linear character, with respectiveslopes of nearly one, passing roughly through the origin. Accordingly,the peaks of the cross-correlation signals of plots 9500 and 9510 may bedetermined to be relatively symmetric, and an associated correlation lagvalue may be qualified based on the symmetry. Plots 9504 and 9514 eachshow a radial curve, using segment lengths equal to period P, whichcorresponds to the cross-correlation signal of plots 9500 and 9510,respectively. Plots 9506 and 9508 show respective radius and anglecalculations based on the radial curve of plot 9504, while plots 9516and 9518 show respective radius and angle calculations based on theradial curve of plot 9514. The radial curves of plots 9504 and 9514 areroughly circular, including a single loop. The radius calculations ofplots 9506 and 9516 show some variation, with radius ratios (e.g.,calculated using Eq. 53) of 1.18 and 1.54, respectively. The anglecalculations of plots 9508 and 9518 give angle sums (e.g., calculatedusing Eq. 55) of 343.4° and 333.7°, respectively. The symmetry curvesand radial curves associated with the cross-correlation results of plots9500 and 9510, and calculations thereof, may indicate that the period Pprovides a relatively good estimate of the actual physiological period.Accordingly, under some circumstances, the correlation lag valueassociated with period P may be qualified.

The bottom two cross-correlation signals of FIG. 95, of plots 9520 and9530, do not exhibit notches and show relatively consistent periodiccharacter. Plots 9522 and 9532 each show a symmetry curve, with lengthsequal to 1.6 times period P, which corresponds to the cross-correlationoutput of plots 9520 and 9530, respectively. The symmetry curves ofplots 9522 and 9532 show substantially non-linear character, withvarying slopes, and deviating relatively far from the origin. The peaksof the cross-correlation output of plot 9520 show varying amplitude,while the peaks of the cross-correlation signal of plot 9530 show anadditional inflection likely caused by a dicrotic notch in the originalphysiological data. Accordingly, the peaks of the cross-correlationsignals of plots 9520 and 9530 may be determined to be relativelyasymmetric, and an associated correlation lag value may be disqualifiedbased on the symmetry. Plots 9524 and 9534 each show a radial curve,using segment lengths equal to period P, which correspond to thecross-correlation signal of plots 9520 and 9530, respectively. Plots9526 and 9528 show respective radius and angle calculations based on theradial curve of plot 9524, while plots 9536 and 9538 show respectiveradius and angle calculations based on the radial curve of plot 9534.The radial curve of plot 9524 shows two loops of substantially differentradii, indicating that the period P may be roughly twice as long as theactual period of cross-correlation signal 9520. The radial curve of plot9534 shows two non-circular loops, of varying radii, indicating that theperiod P may be roughly twice as long as the actual period ofcross-correlation signal 9530. The radius calculations of plots 9526 and9536 show significant variation, with radius ratios (e.g., calculatedusing Eq. 53) of 3.58 and 6.69, respectively. The angle calculations ofplots 9528 and 9538 give angle sums (e.g., calculated using Eq. 55) of757.6° and 703.9°, respectively. The symmetry curves and radial curvesassociated with the cross-correlation results of plots 9520 and 9530,and calculations thereof, may indicate that the period P provides arelatively poor estimate of the actual physiological period. In someembodiments, this may occur when the rate is locked onto low frequencynoise or half the physiological rate or when correlation lag valueidentifies low frequency noise or half the physiological rate.Accordingly, under some circumstances, the correlation lag valueassociated with period P may be disqualified.

In some embodiments, flow diagram 9100 of FIG. 91 and flow diagram 9400if FIG. 94 may operate directly on the physiological data (e.g., ade-trended physiological signal) instead of on a cross-correlationsignal derived from the physiological signal.

In some embodiments, geometric properties or states of the physiologicaldata (e.g., de-trended physiological data) may be analyzed to qualify ordisqualify a correlation lag value. For example, states can bedetermined by analyzing the relationship of a sequence of paired valuesspaced apart in the physiological data and state transitions can beidentified when the states change. FIG. 96 is a flow diagram 9600 ofillustrative steps for qualifying or disqualifying a value that may beindicative of a physiological rate based on a state transition, inaccordance with some embodiments of the present disclosure. Theillustrative steps of flow diagram 9600 may be referred to as the “StateTransition Test.”

Step 9602 may include processing equipment receiving physiological datafrom a physiological sensor, memory, any other suitable source, or anycombination thereof. For example, referring to system 300 of FIG. 3, theprocessing equipment may receive a window of physiological data frominput signal generator 310. Sensor 318 of input signal generator 310 maybe coupled to a subject, and may detect physiological activity such as,for example, RED and/or IR light attenuation by tissue, using aphotodetector. In some embodiments, physiological signals generated byinput signal generator 310 may be stored in memory (e.g., RAM 54 of FIG.2, QSM 72 and/or other suitable memory) after being pre-processed bypre-processor 320. In such cases, step 9602 may include recalling datafrom the memory for further processing.

Step 9604 may include the processing equipment selecting pairs of samplepoints of the physiological data, spaced apart in the physiologicaldata. The spacing may be based on a calculated value indicative of aphysiological rate of the subject. For example, the calculated value maybe based on a correlation lag value, a rate corresponding to thecorrelation lag value, or any other calculated value indicative of aphysiological rate of the subject. In some embodiments, a first portionof physiological data, including a particular number of sample points,may be point-wise paired with a second portion of the physiologicaldata, including the same number of samples points as the first portionalbeit shifted in time relative to the first portion. For example, asshown by panel 9650, the pairs may be generated by selectingcorresponding points of portion 9654 and portion 9656 of physiologicalsignal 9652. In some embodiments, selecting the pairs of points mayinclude specifying indices of the points in the physiological data forreference.

Step 9606 may include the processing equipment determining a state foreach pair of points of the plurality of pairs of step 9604. The statemay be defined by a set of criteria such as, for example, logicaloperations, inequalities, and equalities. For example, referencing table9670, for each pair of values X_(i) and Y_(i), an eight-statecalculation may be performed using the inequality criteria in the secondcolumn of table 9670. The inequality criteria used in table 9670 includecomparisons of the pair values each with zero and with each other. In afurther example, a sixteen-state calculation can be performed byaddition of one or more criteria such as that shown in Eq. 57:CX _(i) >Y _(i) or CX _(i) <Y _(i)  (57)where C is a coefficient used to compare each pair of values X_(i) andY_(i). The value, sign, or both of coefficient C can depend on othercriteria such as the inequality criteria. Note that althoughillustrative Eq. 57 and illustrative table 9670 shows criteria including“greater than” and “less than” inequalities, the criteria may include“greater than or equal,” or “less than or equal” inequalities. Forexample, in the case that X_(i)=Y_(i), the “greater than or equal,” or“less than or equal” inequalities still yield a state value. Anysuitable number of states may be specified using any suitable number andtype of criteria, in accordance with the present disclosure.

Step 9608 may include the processing equipment determining one or morestate transitions based on the determined states of step 9606. In someembodiments, the processing equipment may index through the plurality ofvalue pairs, in order (e.g., in the order of the data points in thefirst segment), determining the number of instances the state changes.For example, for a first segment size of 10 values, corresponding to 10pairs, the states may be 1-1-1-2-2-3-4-5-5-5, for which there are fivestates, and four state transitions among states. As another example, fora segment size of 2 values, corresponding to 2 pairs, the states may be1-3, for which there are two states, but the transition skipped a state.In this example, because state “2” was skipped, this may be counted astwo state transitions. Step 9608 may include the processing equipmentdetermining the total number of state transitions exhibited by theplurality of pairs. The total number of state transitions may be thetotal number of state transitions in one direction. For example, ifthere were eleven state transitions where the state increased and threestate transitions where the state decreased, then the total number ofstate transitions may be determined to be eight. In some embodiments,step 9606 and 9608 may be performed sequentially. In some embodiments,step 9606 and 9608 may be performed substantially at the same time. Forexample, as each state determined for each point, the processingequipment may determine whether a state transition has occurred.

Step 9610 may include the processing equipment qualifying ordisqualifying a correlation lag value, based on the determined statetransitions of step 9608. In some embodiments, the processing equipmentmay determine the number of state transitions and compare the number ofstate transitions to one or more threshold values. For example, if thedetermined number of state transitions is between a lower threshold andan upper threshold, then the processing equipment may qualify thecorrelation lag value.

In an illustrative example, the processing equipment may receivephysiological data, and select pairs of points in the data spaced by onefourth of the calculated value, which may be a previously calculatedcorrelation lag value. The processing equipment may determine a statefor each pair of points, and determine the number of state transitions.Referencing an eight-state calculation, if the processing equipmentdetermines that seven, eight, or nine transitions have occurred, thenthe processing equipment may qualify the correlation lag value. If theprocessing equipment determines that less than six, or more than nine,transitions have occurred then the processing equipment may disqualifythe correlation lag value. It will be understood that the illustrativenumbers and thresholds described in this example are used to describethe technique, although any suitable number of states, and statetransitions may be specified to qualify or disqualify a correlation lagvalue. In some embodiments, the processing equipment may output a onefor a passed State Transition Test (e.g., qualified), and a zero for afailed State Transition Test (e.g., disqualified).

In some embodiments, the processing equipment may generate a metricbased on a history of state transition values stored in a buffer (e.g.,12 number of state transitions values, calculated each second, for theprevious 12 seconds). For example, the processing equipment maydetermine a metric M using an expression such as Eq. 58:M=√{square root over ((NST_(max)−NST_(min)))}(NST_(mean))^(2−K*NST)^(mean)   (58)where NST_(max) is the maximum number of state transitions value storedin the buffer, NST_(min) is the minimum number of state transitionsvalue stored in the buffer, K is a constant, and NST_(mean) is the meannumber of state transitions value stored in the buffer. In someembodiments, the processing equipment may normalize the stored NSTvalues to a predetermined range. This metric analyzes the variability ofthe number of state transitions metric over time. In some situations,the number of state transitions metric may indicate a good number ofstate transitions even though the correlation lag value does notcorrespond to the physiological rate. However, in these situations, thenumber of state transitions metric may vary over time. Accordingly,metric M may be determined and compared to one more thresholds toqualify or disqualify a correlation lag value. Metric M may be used inaddition to or in place of the State Transition Test. It will beunderstood that Eq. 56 is merely illustrative and any suitablevariability metric may be used. In some embodiments, the processingequipment may apply a variability metric such as an expression similarto Eq. 58 to any other suitable noise metric, de-trending metric,qualification metric, or other metric, to determine a variation overtime of the metric.

FIG. 97 is a flow diagram 9700 of illustrative steps for qualifying ordisqualifying a value that may be indicative of a physiological ratebased on a skewness value, in accordance with some embodiments of thepresent disclosure. The illustrative steps of flow diagram 9700 may bereferred to as the “Skewness Test.”

Step 9702 may include processing equipment receiving physiological datafrom a physiological sensor, memory, any other suitable source, or anycombination thereof. For example, referring to system 300 of FIG. 3, theprocessing equipment may receive a window of physiological data frominput signal generator 310. Sensor 318 of input signal generator 310 maybe coupled to a subject, and may detect physiological activity such as,for example, RED and/or IR light attenuation by tissue, using aphotodetector. In some embodiments, physiological signals generated byinput signal generator 310 may be stored in memory (e.g., RAM 54 of FIG.2, QSM 72 and/or other suitable memory) after being pre-processed bypre-processor 320. In such cases, step 9702 may include recalling datafrom the memory for further processing.

Step 9704 may include the processing equipment determining a skewnessmetric value based on the physiological data of step 9702. For example,the processing equipment may use an expression such as that shown in Eq.16 to determine a skewness value. In a further example, the processingequipment may determine a skewness metric other than a third centralmoment of the data, such as an empirical skewness metric indicative ofasymmetry of the a distribution of values of the physiological data.

Step 9706 may include processing equipment qualifying or disqualifying acorrelation lag value, based on the skewness metric value of step 9704.For example, referencing FIG. 22, the processing equipment may determinean expected correlation lag value, or range thereof, based on theskewness value and a reference look-up table, function, or otherreference of related skewness values and expected correlation lagvalues. The processing equipment may compare the expected correlationlag value determined based on the skewness value of step 9704 to acorrelation lag value calculated based on the physiological data. Thecomparison may include comparing the difference in determined andexpected correlation lag values to a threshold. In a further example,the processing equipment may determine probabilities of particularcorrelation lag values, or ranges thereof, based on the determinedskewness value and a reference relationship. If the determinedcorrelation lag value corresponds to a relatively low probability (e.g.,based on a predetermined threshold), the processing equipment maydisqualify the determined correlation lag value. Alternatively, if thedetermined correlation lag value corresponds to a relatively highprobability (e.g., based on a predetermined threshold), the processingequipment may qualify the determined correlation lag value. Similarly,in some embodiments, the processing equipment may determine an expectedskewness value based on a correlation lag value calculated based on thephysiological data, and compare the expected skewness value to thedetermined skewness value of step 9704. In some embodiments, theprocessing equipment may output a one for a passed Skewness Test (e.g.,qualified), and a zero for a failed Skewness Test (e.g., disqualified).Typically, a particular sign of skewness value is expected for a PPGsignal (depending on the signal conditioning and pre-processing), andthe processing equipment may determine the sign of the skew as aQualification Technique. For example, if the sign of the skewness metricvalue of step 9704 agrees with the expected sign the processingequipment may qualify a calculated correlation lag value, and if thesign of the skewness metric value does not agree with the expected signthe processing equipment may disqualify the calculated correlation lagvalue

FIG. 98 is a flow diagram 9800 of illustrative steps for qualifying ordisqualifying one or more values that may be indicative of aphysiological rate based on areas of positive and negative portions ofsegments of a cross-correlation signal, in accordance with someembodiments of the present disclosure. In some embodiments, theillustrative steps of flow diagram 9800 aid in preventing a double-rate(half-period) condition, half-rate (double-period) condition, or otherconditions in which period P is not indicative of a physiological rate.The illustrative steps of flow diagram 9800 may be referred to as the“Area Test.”

Step 9802 may include processing equipment receiving a cross-correlationsignal (e.g., generated according to step 8606 of FIG. 86) as an input.In some embodiments, the cross-correlation signal may be generated by across-correlation module. In some embodiments the cross-correlationsignal may be generated at an earlier time, and stored in suitablememory. Accordingly, in some embodiments, step 9802 may includerecalling the stored cross-correlation signal from the memory. In someembodiments, a single processor, module, or system may perform thecross-correlation and steps 9804-9814, and accordingly, step 9802 neednot be performed.

Step 9804 may include the processing equipment selecting two signalsegments of the cross-correlation signal of step 9802. The first segmentmay be half as large as the second segment. For example, the secondsegment may have a length equal to period P, while the first segment hasa length equal to one half of period P and is included in the secondsegment. In a further example, the second segment may have a lengthequal to period 2P, while the first segment has a length equal to periodP. The two segments may be selected from any suitable portion of thecross-correlation output.

Step 9806 may include the processing equipment calculating the positiveand negative areas of both the first and second segments. Each segmentmay include one or more oscillations, or portions thereof, andaccordingly may include positive and negative portions (e.g., FIGS.99-101 provides further illustration). In some embodiments, step 9806may include performing a summation of the positive data points, and asummation of the negative data points for each segment. In someembodiments, step 9806 may include performing a piecewise discreteintegration (e.g., a piecewise numerical quadrature or any othersuitable analytical or numerical technique) of the positive and negativeportions of each of the two segments of the cross-correlation signal. Insome embodiments, the area values of step 9806 may both be positivevalues, with the areas of a negative portion of a segment calculated bytaking the absolute value of the integral of the negative portion. Insome embodiments, either or both areas may be normalized by theassociated segment length. This normalization may allow for a particularthreshold to be used for segments having different lengths. For example,the areas of a segment of length P may both be divided by P or one. In afurther example, the areas of a segment of length 2P may both be dividedby 2P or two.

Step 9808 may include the processing equipment determining the absolutevalue of the difference between the positive and negative areas (whichmay both be positive values) for each segment. In some embodiments, theprocessing equipment may calculate the difference between the areas ofthe positive and negative portions of each segment by subtracting thearea of the negative portion, which may be a positive value, from thearea of the positive portion (or vice versa), and then calculating theabsolute value of the difference.

Step 9810 may include the processing equipment repeating steps9804-9808, for various shifts in the segments relative to one another,relative to a varying origin point with the segments' relative positionsfixed, or any other suitable shift. In some embodiments, the first andsecond segments may always originate at the same point, which may beshifted along the cross-correlation output at step 9810 to define thedifferent pairs of segments. In some embodiments, the first segment mayremain fixed and the second segment may be shifted at step 9810. Forexample, the first segment, with a length equal to P/2, may remain fixedand the second segment, with a length equal to P, may be shifted by oneor more degree measures and the difference may be calculated at eachdegree measure. In a further example, the second segment, with a lengthequal to P, may remain fixed and the first segment, with a length equalto P/2, may be shifted by one quarter of the period P, and thedifference may be calculated at this new segment location. Either orboth of the first and second segments may be shifted any suitable amountto generate one or more pairs of first and second segments. In someembodiments, step 9808, step 9810, or both, may include storing the oneor more respective difference values of steps 9808 and 9810 in anysuitable memory, using any suitable data-basing or filing protocol.

Step 9812 may include the processing equipment identifying maximumvalues, minimum values, or both, of the differences among both the firstsegments and the second segments. For example, in some embodiments, theprocessing equipment may identify the maximum difference of the firstsegment(s) and the minimum difference of the second segment(s). In someembodiments, the processing equipment may identify the maximumdifference of the first segment(s) and the minimum difference of thesecond segment(s). Any suitable technique may be used to identify therespective maximum and minimum values. In some embodiments, step 9812may be performed concurrent with steps 9808 and 9810. For example,initial maximum and minimum difference values may be calculated at step9808 and stored. As subsequent difference values are calculated at step9810, the stored values may be replaced as larger or smaller values, asappropriate, are calculated.

Step 9814 may include the processing equipment qualifying ordisqualifying the correlation lag values, based on the identifiedmaximum and minimum differences of step 9812. In some embodiments, theprocessing equipment may calculate a difference between the maximumdifference for the first segments and the minimum difference for thesecond segments. For example, one or more thresholds may be set based onthe value of the minimum difference for the second segments (e.g., thethreshold value may be equal to the minimum difference value or ascaling thereof). The maximum difference for the first segment may thenbe compared to the threshold. If the maximum difference is greater thanthe threshold, the processing equipment may qualify the correlation lagvalue. If the maximum difference is less than the threshold, theprocessing equipment may disqualify the correlation lag value. In someembodiments, the processing equipment may calculate a ratio of themaximum difference for the first segment to the minimum difference forthe second segment. For example, one or more thresholds, which may bepredetermined or may depend upon the difference values, may be set. Theratio may then be compared to the threshold. If the ratio is greaterthan the threshold, the processing equipment may qualify the correlationlag value. If the ratio is less than the threshold, the processingequipment may disqualify the correlation lag value. In some embodiments,the ratio may be calculated for each pair of first and second segments,and a maximum ratio value may be selected to be compared with one ormore threshold values. In some embodiments, the areas, differences, orratios may be normalized by dividing by a value corresponding to thesegment length. In some embodiments, the thresholds may be scaled basedon the segment lengths of the period P. In some embodiments, one or moremetrics may inputted into a classifier.

In some embodiments, the illustrative steps of flow diagram 9800 may beperformed using segments of length P/2 and P, which may provideparticular benefits under some circumstances. In some embodiments, theillustrative steps of flow diagram 9800 may be performed using segmentsof length P and 2P, which may provide particular benefits under somecircumstances. Any suitable first and second segments may be used toperform the Area Test. The following discussion, referencing FIGS.99-101, provides further detail regarding flow diagram 9800, inaccordance with some embodiments of the present disclosure. The abscissaof each of plots 9900, 10000, and 10100 of FIGS. 99-101, respectively,are presented in units proportional to cross-correlation lag, while theordinate is presented in arbitrary units, with zero notated.

FIG. 99 is a plot 9900 of an illustrative cross-correlation output 9902,centered about zero with a correctly determined correlation lag value,in accordance with some embodiments of the present disclosure. Theperiod P is close to the period exhibited by cross-correlation signal9902. As shown in plot 9900, cross-correlation signal 9902 may includeportions above the baseline (i.e., values greater than zero), andportions below the baseline (i.e., values less than zero). Depending ona how a segment of cross-correlation signal 9902 is selected, the areasof the positive and negative portions may change relative to oneanother.

Three illustrative segment lengths are shown in FIG. 99, includingperiod P, one half of period P, and double period P. The difference inthe areas of the positive and negative portions of segments of length Por 2P may be expected to be relatively small for any suitable shiftbecause the segments approximately cover a complete period or doubleperiod exhibited by cross-correlation output 9902. The differencebetween the areas of the positive and negative portions of segments oflength P/2 may be expected to range from small values (e.g., when asegment is centered near a zero of cross-correlation signal 9902) torelatively larger values (e.g., when the segment lies substantiallybetween zeros of cross-correlation signal 9902).

In some cases, in which the correlation lag value is correctlydetermined, a first segment may have a length equal to P/2 and thesecond segment may have a length equal to period P. Differences in areasof positive and negative portions of cross-correlation signal 9902 maybe calculated for multiple first and second segments, having constantrespective lengths but varying shift. In some such cases, the maximumdifference among first segments may be compared to the minimumdifference among second segments, or a threshold derived thereof. Forexample, the maximum difference among first segments may be compared toa threshold equal to four times the minimum difference of the secondsegments. If the maximum difference among first segments is larger thanthe threshold, then the correlation lag value may be qualified.

In some cases, in which the correlation lag value is correctlydetermined, a first segment may have a length equal to P and the secondsegment may have a length equal to period 2P. Unlike the previouslydiscussed cases, in which first and second segments had respectivelengths of P/2 and P, the differences of the present cases are now bothexpected to be small values. Accordingly, in some such cases, themaximum difference among first segments may be compared to the minimumdifference among second segments, or a threshold derived thereof. Forexample, the maximum difference among first segments may be compared toa threshold equal to two times the minimum difference of the secondsegments. If the maximum difference among first segments is smaller thanthe threshold, then the correlation lag value may be qualified.

FIG. 100 is a plot 10000 of an illustrative cross-correlation output10002 with a correlation lag value incorrectly determined to be doublethe correct rate, in accordance with some embodiments of the presentdisclosure. The period P is close to one half of the period exhibited bycross-correlation signal 10002. Three illustrative segment lengths areshown in FIG. 100, including period P, one half of period P, and doubleperiod P. As illustrated, the difference in the areas of the positiveand negative portions of segments of length 2P may be expected to berelatively small for any suitable shift. The difference in the areas ofthe positive and negative portions of segments of lengths P/2 or P maybe expected to range from small values (e.g., when a segment is centerednear a zero of cross-correlation signal 10002) to relatively largervalues (e.g., when the segment lies substantially between zeros ofcross-correlation signal 10002).

In some cases, in which the correlation lag value is incorrectlydetermined, a first segment may have a length equal to P/2 and thesecond segment may have a length equal to period P. Differences in areasof positive and negative portions of cross-correlation signal 10002 maybe calculated for multiple first and second segments, having constantrespective lengths but varying shift. In some such cases, the maximumdifference among first segments may be relatively large compared to theminimum difference among second segments. Accordingly, under somecircumstances, the use of first and second segments with respectivelengths P/2 and P may allow a double-rate condition to qualify ifsimilar threshold conditions are used as when the correlation lag valueis correctly determined.

In some cases, in which the correlation lag value is incorrectlydetermined, a first segment may have a length equal to period P and thesecond segment may have a length equal to period 2P. In some such cases,the maximum difference of the first segment may be compared to theminimum difference of the second segment, or a threshold derivedthereof. If the maximum difference among first segments is larger than asuitable threshold (e.g., the same threshold condition as used when thecorrelation lag value is correctly determined), as may be expected, thenthe correlation lag value may be disqualified. Accordingly, in someembodiments, the Area Test may provide techniques to disqualify adouble-rate condition.

FIG. 101 is a plot 10100 of an illustrative cross-correlation signal10102 with the correlation lag value incorrectly determined to be halfthe correct rate, in accordance with some embodiments of the presentdisclosure. The period P is close to twice the period exhibited bycross-correlation signal 10102. Three illustrative segment lengths areshown in FIG. 101, including period P, one half of period P, and doubleperiod P. As illustrated, the difference in the areas of the positiveand negative portions of segments of length P/2, P, and 2P may beexpected to be relatively small for any suitable shift. In some cases,any full period of cross-correlation signal 10102 may provide arelatively low difference value, regardless of shift.

In some cases, in which the correlation lag value is incorrectlydetermined to be one half the correct rate, a first segment may have alength equal to P/2 and the second segment may have a length equal toperiod P. Differences in areas of positive and negative portions ofcross-correlation signal 10102 may be calculated for multiple first andsecond segments, having constant respective lengths but varying shift.In some such cases, the maximum difference among first segments may becomparable to the minimum difference among second segments. If themaximum difference among first segments is smaller than the threshold(e.g., the same threshold condition as used when the correlation lagvalue is correctly determined), then the correlation lag value may bedisqualified. Accordingly, the Area Test may provide techniques todisqualify a half-rate condition.

In some cases, in which the correlation lag value is incorrectlydetermined to be one half the correct rate, a first segment may have alength equal to period P and the second segment may have a length equalto period 2P. In some such cases, the maximum difference of the firstsegment may be compared to the minimum difference of the second segment,or threshold derived thereof. If the maximum difference among firstsegments is smaller than a suitable threshold (e.g., the same thresholdcondition as used when the correlation lag value is correctlydetermined), as may be expected, then the correlation lag value may bequalified. Accordingly, under some circumstances, the use of first andsecond segments with respective lengths P and 2P may allow a half-ratecondition to qualify if similar threshold conditions are used as whenthe correlation lag value is correctly determined.

In view of the foregoing, first and second segments with respectivelengths P/2 and P may prevent qualification of a half-rate condition andfirst and second segment lengths of P and 2P may prevent qualificationof a double-rate condition. Accordingly, in some embodiments, the AreaTest may provide techniques to prevent qualification of double-rate,half-rate, or any other rate condition that is not indicative of aphysiological rate.

FIG. 102 is a plot 10200 of illustrative difference calculations of theArea Test with a correctly determined correlation lag value, and a plot10250 of illustrative calculated rates indicative of an actualphysiological heart rate, in accordance with some embodiments of thepresent disclosure. The abscissa of plot 10200 is presented in units ofseconds, while the ordinate is presented in arbitrary units, with zeronotated. Series 10202 is the maximum difference in first segments, withlengths equal to one half of period P. Series 10204 is the minimumdifference in second segments, with lengths equal to period P. Threshold10206 is an illustrative threshold equal to four times series 10204(i.e., scaled from series 10204 by a multiplicative factor of four). Asshown in FIG. 102, series 10202 lies above threshold 10206, indicatingthat the correlation lag value may be qualified. Plot 10250 illustratescalculated rates indicative of an actual physiological rate, inaccordance with some embodiments of the present disclosure. The abscissaof plot 10250 is presented in units of seconds, while the ordinate ispresented in units of BPM. Time series 10252 is the pulse rate (i.e.,BPM) as calculated by a subject monitoring system tracking the actualrate, shown by time series 10254.

FIG. 103 is a flow diagram of illustrative steps for qualifying ordisqualifying one or more values that may be indicative of aphysiological rate based on a filtered physiological signal, inaccordance with some embodiments of the present disclosure. In someembodiments, performance of the illustrative steps of flow diagram 10300may provide an indication of the relative energy in a high frequencycomponent of a physiological signal. Accordingly, in some circumstances,relatively large amounts of energy at rates much larger than the rateassociated with the correlation lag value may indicate thatlow-frequency noise has been locked onto. The illustrative steps of flowdiagram 10300 may be referred to as the “High Frequency Residual Test.”

Step 10302 may include processing equipment receiving at least onephysiological signal from a physiological sensor, memory, any othersuitable source, or any combination thereof. For example, referring tosystem 300 of FIG. 3, processor 312 may receive a physiological signalfrom input signal generator 310. Sensor 318 of input signal generator310 may be coupled to a subject, and may detect physiological activitysuch as, for example, RED and IR light attenuation by tissue, using aphotodetector. In some embodiments, for example, physiological signalsgenerated by input signal generator 310 may be stored in memory (e.g.,memory of system 10 of FIGS. 1-2) after being pre-processed bypre-processor 320. In such cases, step 10302 may include recalling thesignals from the memory for further processing. The physiological signalof step 10302 may include a PPG signal, which may include a sequence ofpulse waves and may exhibit motion artifacts, noise from ambient light,electronic noise, system noise, any other suitable signal component, orany combination thereof. Step 10302 may include receiving a particulartime interval or corresponding number of samples of the physiologicalsignal. In some embodiments, step 10302 may include receiving adigitized, sampled, and pre-processed physiological signal.

Step 10304 may include the processing equipment determining one or moresignal metrics for the physiological signal of step 10302. Signalmetrics may include a standard deviation of the physiological signal, anRMS of the physiological signal, an amplitude of the physiologicalsignal, a sum or integral of the physiological signal over time orsamples, any other suitable metric indicative of a magnitude of a signalor change thereof, or any combination thereof. In some embodiments, theprocessing equipment may compute the standard deviation of thephysiological signal, which may indicate the relative amplitude ofexcursions in the signal about the mean. In a further example, theprocessing equipment may take an absolute value of a physiologicalsignal with zero mean, and then integrate the resulting signal over timeor sample number to provide an indication of the magnitude of signalexcursions about the mean (e.g., zero).

Step 10306 may include the processing equipment filtering thephysiological signal of step 10302. In some embodiments, the processingequipment may apply a high-pass filter to the physiological signal ofstep 10302. For example, the processing equipment may apply a high-passfilter (e.g., having any suitable order and spectral characteristics)having a cutoff at double the rate associated with the one or morecorrelation lag values. In some embodiments, the processing equipmentmay apply a notch filter to the physiological signal of step 10302. Forexample, the processing equipment may apply a notch filter (e.g., havingany suitable spectral characteristics) having a notch centered at doublethe rate associated with the one or more correlation lag values. In someembodiments, the processing equipment may apply a high-pass filter and anotch filter to the physiological signal of step 10302. For example, theprocessing equipment may apply a high-pass filter having a cutoff atdouble the rate associated with the correlation lag value, and a notchfilter having a notch centered at double the rate associated with theone or more correlation lag values. The resulting high frequency (HF)signal may be further analyzed at step 10308. It will be understood thatthe physiological signal may undergo additional filtering before orafter step 10306. Accordingly, the “filtered signal” of step 10306refers to the filtering performed at step 10306, and not any previous orsubsequent filtering.

Step 10308 may include the processing equipment determining one or moresignal metrics for the filtered signal of step 10306. Signal metrics mayinclude a standard deviation of the filtered signal, an RMS of thefiltered signal, an amplitude of the filtered signal, a sum or integralof the filtered signal over time or samples, any other suitable metricindicative of a magnitude of a signal or change thereof, or anycombination thereof. In some embodiments, the processing equipment maycompute the standard deviation of the filtered signal, which mayindicate the relative amplitude of excursions in the signal about themean. In a further example, the processing equipment may take anabsolute value of a filtered signal with zero mean, and then integratethe resulting signal over time or sample number to provide an indicationof the magnitude of signal excursions about the mean (e.g., zero).

Step 10310 may include the processing equipment comparing the one ormore signal metrics for the physiological signal of step 10302 with theone or more signal metrics for the filtered signal of step 10306. Insome embodiments, the processing equipment may determine a comparisonmetric. In some embodiments, the processing equipment may determine theratio of the signal metric(s) for the filtered signal to the signalmetric(s) for the physiological signal. For example, the processingequipment may calculate the ratio of standard deviations of the filteredsignal to the physiological signal. In some embodiments, the processingequipment may determine the difference between the signal metric(s) forthe filtered signal and the signal metric(s) for the physiologicalsignal.

Step 10312 may include the processing equipment qualifying ordisqualifying correlation lag values based on the comparison of step10310. In some embodiments, the processing equipment may compare thecomparison metric of step 10310 with one or more threshold values. Forexample, the processing equipment may compare the ratio of standarddeviations of the filtered signal to the physiological signal to a ratiothreshold. The ratio threshold may be any suitable fixed or adjustablevalue. For example, the ratio threshold may depend on the correlationlag value. If the ratio is larger than the ratio threshold, theprocessing equipment may disqualify the correlation lag value. If theratio is smaller than the ratio threshold, the processing equipment mayqualify the correlation lag value. Accordingly, the processing equipmentmay use the High Frequency Residual Test to determine conditions havingrelatively large amounts of signal energy at rates significantly largerthan the rate associated with one or more correlation lag values.

FIGS. 104 and 105 are illustrative flow diagrams of techniques that mayalso be used to qualify or disqualify one or more correlation lagvalues. In some embodiments, the illustrative steps of flow diagrams10400 and 10500 of FIGS. 104 and 105, respectively, may be performedbased on a correlation lag value. In some embodiments, the illustrativesteps of flow diagrams 10400 and 10500 of FIGS. 104 and 105,respectively, may be performed independent of a correlation lag value(e.g., to quantify noise and/or consistency in a buffered signal).

FIG. 104 is a flow diagram 10400 of illustrative steps for qualifying ordisqualifying one or more values that may be indicative of aphysiological rate based on a comparison of areas of two segments of across-correlation signal, in accordance with some embodiments of thepresent disclosure. In some embodiments, performance of the illustrativesteps of flow diagram 10400 may provide an indication of the similaritybetween different segments of a cross-correlation signal. Theillustrative steps of flow diagram 10400 may be referred to as the “AreaSimilarity Test.”

Step 10402 may include processing equipment receiving across-correlation signal (e.g., generated according to step 8606 of FIG.86) as an input. In some embodiments, the cross-correlation signal maybe generated by a cross-correlation module. In some embodiments thecross-correlation signal may be generated at an earlier time, and storedin suitable memory. Accordingly, in some embodiments, step 10402 mayinclude recalling the stored cross-correlation signal from the memory.In some embodiments, a single processor, module, or system may performthe cross-correlation and steps 10404-10408, and accordingly, step 10402need not be performed.

Step 10404 may include the processing equipment selecting two segmentsof the cross-correlation signal of step 10402. In some embodiments, thetwo segments may be of equal length. In some such embodiments, thecross-correlation signal may be equi-partitioned into a right segmentand a left segment. For example, if the cross-correlation signal is sixseconds in length, the left segment may be the left three seconds andthe right segment may be adjacent right three seconds of the signal. Ina further example, the first and second segments may each have a lengthequal to period P. Any suitable segments may be selected in accordancewith the present disclosure. Note that the two segments will be referredto as Segment1 and Segment2, or the first segment and the secondsegment, although the designations are arbitrarily assigned forillustration purposes (e.g., the first and second segments may beinterchanged in accordance with the present disclosure).

Step 10406 may include the processing equipment calculating the area ofeach of the first and second segments of step 10404. In someembodiments, step 10406 may include calculating an integral (e.g., aquadrature or any other suitable analytical or numerical technique),sum, or both, of the first and second segments. In some embodiments, thecalculated area may be additive among the positive and negative areas.For example, the area of positive portions and the absolute value of thearea of negative portions may be summed, resulting in a positive result.In some embodiments, the calculated area may be subtractive, in whichthe area of positive portions and negative portions of each segment arerespective positive and negative numbers (e.g., integral of the segmentvalues in which negative portions contribute negative integrals), and aresulting sum may be positive or negative.

Step 10408 may include the processing equipment qualifying ordisqualifying a correlation lag value based on a comparison of thecalculated areas of step 10406. Any suitable comparison technique,including the calculation of any suitable comparison metric (e.g.,difference, ratio), may be used to compare the areas of the twosegments. In some embodiments, the processing equipment may calculate adifference between the area of the first segment and the area of thesecond segment (or vice versa). In some embodiments, the processingequipment may calculate a ratio of the area of the first segment to thearea of the second segment (or vice versa). In some embodiments,qualification or disqualification may include comparing a comparisonmetric to a threshold value. For example, the difference between (orratio of) the areas of the two segments may be calculated and if above athreshold value, the correlation lag value may be disqualified. Usingsuitable segment selection, the areas of the two segments may beexpected to be similar, if period P provides a relatively accurateindication of a physiological pulse period.

In some embodiments, step 10408 need not be performed with steps10402-10406. In some embodiments, steps 10402-10406 may be performedindependent of a correlation lag value. For example, a cross-correlationsignal of a buffered window of data may be analyzed using steps10402-10406. The areas of two segments (e.g., fixed length segments) ofthe cross-correlation signal may be compared using a comparison metric,and under some circumstances the buffered data may be qualified ordisqualified (e.g., based on a comparison of the comparison metric witha threshold). This may be particularly useful when the physiologicalsignal is relatively noise free and then noise suddenly appears in thesignal. This may also be particularly useful when strong non-periodicnoise is present in the signal.

FIG. 105 is a flow diagram 10500 of illustrative steps for qualifying ordisqualifying a value that may be indicative of a physiological ratebased on statistical properties of a cross-correlation signal, inaccordance with some embodiments of the present disclosure. In someembodiments, performance of the illustrative steps of flow diagram 10500may provide an indication of the similarity between positive andnegative portions of a cross-correlation signal. The illustrative stepsof flow diagram 10500 may be referred to as the “Statistical PropertyTest.”

Step 10502 may include processing equipment receiving across-correlation signal (e.g., generated according to step 8606 of FIG.86) as an input. In some embodiments, the cross-correlation signal maybe generated by a cross-correlation module. In some embodiments thecross-correlation signal may be generated at an earlier time, and storedin suitable memory. Accordingly, in some embodiments, step 10502 mayinclude recalling the stored cross-correlation signal from the memory.In some embodiments, a single processor, module, or system may performthe cross-correlation and steps 10504-10508, and accordingly, step 10502need not be performed.

Step 10504 may include the processing equipment selecting the positivevalues of the cross-correlation signal, and selecting the negativevalues of the cross-correlation signal. For example, the processingequipment may compare each value of the cross-correlation signal tozero, and use the comparison to select positive and/or negative values.

Step 10506 may include the processing equipment calculating astatistical property of the positive values and of the negative valuesof step 10504. The statistical property may include a mean, standarddeviation, variance, root-mean-square (RMS) deviation (e.g., relative tozero), any other suitable statistical property, or any combinationthereof. In some embodiments, the positive values and negative valuesmay be processed separately at step 10506.

Step 10508 may include the processing equipment qualifying ordisqualifying the one or more correlation lag values based on acomparison of the statistical properties of step 10506. Any suitablecomparison technique, including the calculation of any suitablecomparison metric (e.g., difference, ratio), may be used to compare thestatistical properties of the positive and negative values. In someembodiments, the processing equipment may calculate a difference betweenthe statistical properties of the positive and negative values (or viceversa). In some embodiments, the processing equipment may calculate aratio of the statistical properties of the positive and negative values(or vice versa). In some embodiments, qualification or disqualificationmay include comparing a comparison metric to a threshold value. Forexample, the difference between (or ratio of) the statistical propertiesof the positive and negative values may be calculated and if above athreshold value, the correlation lag value may be disqualified.

In some embodiments, step 10508 need not be performed with steps10502-10506. In some embodiments, steps 10502-10506 may be performedindependent of a correlation lag value. For example, a cross-correlationsignal of a buffered window of data may be analyzed using steps10502-10506. The statistical properties of the positive and negativevalues of the cross-correlation output may be compared using acomparison metric, and under some circumstances the buffered data may bequalified or disqualified (e.g., based on a comparison of the comparisonmetric with a threshold).

FIG. 106 is a flow diagram 10600 of illustrative steps for qualifying ordisqualifying a value that may be indicative of a physiological ratebased on differences of a physiological signal, in accordance with someembodiments of the present disclosure. The illustrative steps of flowdiagram 10600 may be referred to as the “Integral Test.”

Step 10602 may include processing equipment receiving physiological datafrom a physiological sensor, memory, any other suitable source, or anycombination thereof. For example, referring to system 300 of FIG. 3, theprocessing equipment may receive a window of physiological data frominput signal generator 310. Sensor 318 of input signal generator 310 maybe coupled to a subject, and may detect physiological activity such as,for example, RED and/or IR light attenuation by tissue, using aphotodetector. In some embodiments, physiological signals generated byinput signal generator 310 may be stored in memory (e.g., RAM 54 of FIG.2, QSM 72 and/or other suitable memory) after being pre-processed bypre-processor 320. In such cases, step 10602 may include recalling datafrom the memory for further processing.

Step 10604 may include the processing equipment receiving a calculatedvalue indicative of a physiological rate of the subject. For example,the calculated value may be based on a correlation lag value, a ratecorresponding to the correlation lag value, or any other calculatedvalue indicative of a physiological rate of the subject. In someembodiments, step 10604 may include recalling the calculated value frommemory for further processing.

Step 10606 may include the processing equipment determining a sequenceof difference values between samples points and corresponding samplepoints spaced apart based on the calculated value of step 10604. Forexample, the processing equipment may specify a segment of thephysiological data, and determine the difference between each value ofthe segment and a corresponding sample point of the physiological dataspaced by a shift. The calculated value may be a calculated correlationlag value, and the shift may be equal to the correlation lag value. Theprocessing equipment may use an expression such as, for example, Eq. 59:D _(i) =X _(i) −X _(i-N)  (59)to determine the sequence of difference values D_(i), indexed by i,where X_(i) is a sample point and X_(i-N) is a sample point spaced by Npoints (e.g., where N corresponds to the correlation lag value). In someembodiments, the processing equipment may determine absolute values ofthe sequence of differences, thus resulting in positive values of thedifferences.

Step 10608 may include the processing equipment determining a sum basedon the sequence of differences of step 10606. In some embodiments, theprocessing equipment may sum the sequence of difference values to obtaina single summation value. In some embodiments, the processing equipmentmay perform steps 10606 and 10608 by determining a root-mean-square(RMS) difference between the sample values and corresponding samplevalues. The sum may include any suitable mathematical combination of thecollective differences of the sample points and corresponding samplepoints, and may be optionally normalized (e.g., by the number of samplepoints).

Step 10610 may include processing equipment qualifying or disqualifyingthe correlation lag value of step 10606, based on the sum of step 10608.In some embodiments, the sum may be compared to a threshold. If the sumis greater than the threshold, the processing equipment may disqualifythe correlation lag value, while if the sum is less than the threshold,the processing equipment may qualify the correlation lag value.Accordingly, the sum may be expected to be relatively low when thephysiological data is relatively more periodic, typically indicating asegment of data is relatively more similar to a segment spaced by aperiod corresponding to a physiological rate.

FIG. 107 is a flow diagram 10700 of illustrative steps for qualifying ordisqualifying a value that may be indicative of a physiological ratebased on a half lag analysis, in accordance with some embodiments of thepresent disclosure. The illustrative steps of flow diagram 10700 may bereferred to as the “Half Lag Test.”

Step 10702 may include processing equipment receiving physiological datafrom a physiological sensor, memory, any other suitable source, or anycombination thereof. For example, referring to system 300 of FIG. 3, theprocessing equipment may receive a window of physiological data frominput signal generator 310. Sensor 318 of input signal generator 310 maybe coupled to a subject, and may detect physiological activity such as,for example, RED and/or IR light attenuation by tissue, using aphotodetector. In some embodiments, physiological signals generated byinput signal generator 310 may be stored in memory (e.g., RAM 54 of FIG.2, QSM 72 and/or other suitable memory) after being pre-processed bypre-processor 320. In such cases, step 10702 may include recalling datafrom the memory for further processing.

Step 10704 may include the processing equipment determining acorrelation lag value of the physiological data of step 10702. In someembodiments, the processing equipment may generate a correlationsequence. The correlation sequence may include a sequence of correlationvalues corresponding to different lag values. In some embodiments, theprocessing equipment may generate the correlation sequence bymultiplying values of a first segment of the physiological segment withcorresponding values of a second segment of the physiological data,shifted in time by a particular lag, for multiple lag values. Anysuitable technique in may be used to determine the correlation lag valuesuch as, for example, the illustrative techniques discussed in thecontext of FIGS. 62-85, or any other suitable technique or combinationof techniques thereof. For example, the processing equipment mayidentify a peak in the correlation sequence, and may determine thecorrelation lag value based on the identified peak.

Step 10706 may include the processing equipment determining acorrelation value at a lag of substantially one half of the determinedcorrelation lag value of step 10704. In some embodiments, the processingequipment may generate a correlation sequence at step 10704, and selectthe half lag value at step 10706. In some embodiments, the processingequipment may perform a correlation calculation at the half lag value todetermine the correlation value.

Step 10708 may include processing equipment qualifying or disqualifyingthe correlation lag value of step 10706, based on the correlation valueat a lag of substantially one half of the determined correlation lagvalue of step 10704. In some embodiments the correlation at the half lagvalue may be compared with a threshold, and if the correlation at lagexceeds the threshold, the correlation lag value is disqualified. Insome embodiments, the correlation at the half lag value may be comparedwith the correlation value at the correlation lag value, and if thedifference is greater than a threshold, the correlation lag value may bequalified. The correlation at the half lag value may be expected to berelatively less than the correlation value at the correlation lag value,and may be expected to be less than zero in some circumstances.

FIG. 108 is a flow diagram 10800 of illustrative steps for qualifying ordisqualifying a value that may be indicative of a physiological ratebased on a sorted difference signal, in accordance with some embodimentsof the present disclosure. The illustrative steps of flow diagram 10800may be referred to as the “Ordered Statistic Test.” The OrderedStatistic Test (OS Test) uses up to four segments of physiological datato determine whether to qualify a correlation lag value. The OS Testcompares segments of data to determine if the correlation lag value iscorrect, if the window of physiological data is valid, or both. Forexample, the OS Test may analyze a left half and a right half of awindow of data. Further to this example, the OS Test may analyze asegment of the left half of a size equal to a calculated correlation lagvalue and a segment of the right half of a size equal to a calculatedcorrelation lag value. Each analysis may include determining one or moremetrics based on the physiological data, generating a sorted differencesignals and determining one or metrics based on the sorted differencesignals, pairing the sorted difference signals and determining one ormore metrics based on the value pairs, or any other suitable analyses.If the calculated correlation lag value substantially corresponds to aperiod of the physiological rate, and the physiological data does notexhibit significant noise or artifacts, the analysis of the left andright halves and the analysis of the two segments should produce similarresults (e.g., metric values) when the OS Test is applied. Analysis ofthe left and right halves may provide an indication of the noise levelin the physiological data (e.g., as described in the discussion of FIG.37), for example. Analysis of the two segments of size equal to theperiod associated with the physiological rate, and/or comparison to theanalysis of the halves, may provide an indication of whether thecalculated correlation lag value is correct, for example.

Step 10802 may include processing equipment receiving physiological datafrom a physiological sensor, memory, any other suitable source, or anycombination thereof. For example, referring to system 300 of FIG. 3, theprocessing equipment may receive a window of physiological data frominput signal generator 310. Sensor 318 of input signal generator 310 maybe coupled to a subject, and may detect physiological activity such as,for example, RED and/or IR light attenuation by tissue, using aphotodetector. In some embodiments, physiological signals generated byinput signal generator 310 may be stored in memory (e.g., RAM 54 of FIG.2, QSM 72 and/or other suitable memory) after being pre-processed bypre-processor 320. In such cases, step 10802 may include recalling datafrom the memory for further processing.

Step 10804 may include the processing equipment receiving a calculatedvalue indicative of a physiological rate of the subject. For example,the calculated value may be based on a correlation lag value, a ratecorresponding to the correlation lag value, or any other calculatedvalue indicative of a physiological rate of the subject. In someembodiments, step 10804 may include recalling the calculated value frommemory for further processing.

Steps 10806, 10808, 10810, and 10812 may include the processingequipment identifying a first segment of the physiological data, asecond segment of the physiological data, a third segment of thephysiological data, and a fourth segment of the physiological data,respectively. The first and second segments may be the same size (e.g.,the same number of sample points), and the third and fourth segments maybe the same size (e.g., the same number of sample points), notnecessarily the same as the size of the first and second segments. Insome embodiments, the processing equipment may identify a segment byidentify indices of a sequence of physiological data points. In someembodiments, the processing equipment may identify the first segment,and then identify the second, third, and fourth segments relative to thefirst segment. In some embodiments, the processing equipment mayidentify the first segment and the third segment, and then identify thesecond segment and the fourth segment relative to the first segment andthe second segment, respectively. For example, the processing equipmentmay receive a six second window of data at step 10802, and partition thedata into two three-second segments as the first and second segments.Further to this example, the processing equipment may identify a smallersegment (e.g., having a size corresponding to the calculated value ofstep 10804) from the first segment as the third segment, identify asmaller segment (e.g., having a size corresponding to the calculatedvalue of step 10804) from the second segment as the fourth segment. Toillustrate the previous example, FIG. 109 is a block diagram ofillustrative physiological data 10902 (e.g., from a PPG signal) and fouridentified segments 10904, 10906, 10908, and 10910, in accordance withsome embodiments of the present disclosure. Segments 10904 and 10906 arethe first and second segments, while segments 10908 and 10910 are thethird and fourth segments.

Steps 10814, 10816, 10818, and 10820 may include the processingequipment generating a first difference signal based on the firstsegment, a second difference signal based on the second segment, a thirddifference signal based on the third segment, and a fourth differencesignal based on the fourth segment, respectively. In some embodiments,the processing equipment may perform a subtraction between values ofadjacent samples of a segment to generate the respective differencesignal. In some embodiments, the processing equipment may calculatedifferences by calculating a first derivative of the respective segment.For example, the processing equipment may compute forward differences,backward differences, or central differences between each pair ofadjacent points to generate a difference signal. In a further example,the processing equipment may compute a numerical derivative at eachpoint in the segment, generating a difference signal. Any suitabledifference technique may be used by the processing equipment to generatethe difference signals.

Steps 10822, 10824, 10826, and 10828 may include processing equipmentsorting the difference values of each difference signal of steps 10814,10816, 10818, and 10820, respectively. The processing equipment may sortthe values in ascending or descending order, either of which causes thenegative and positive values to be separated. Referencing sorted valuesin ascending order, the most negative values come first followed by lessnegative values, positive values, and finally larger positive values.The output of steps 10822, 10824, 10826, and 10828 may be a first sorteddifference signal, a second sorted difference signal, a third sorteddifference signal, and a fourth sorted difference signal, respectively.

In an illustrative example of the sorted difference signals of steps10822, 10824, 10826, and 10828, FIG. 110 is a panel of illustrativeplots showing paired sorted difference signals 11002, 11004, 11006,11008, 11010, 11012, 11014, and 11016, in accordance with someembodiments of the present disclosure. Paired sorted difference signals11002 and 11004 represent value pairs of first and second sorteddifference signals, and value pairs third and fourth sorted differencesignals, respectively, derived from physiological data exhibiting adicrotic notch. The value pairs are generated by pairing correspondingpoints of the respective segments of the same size. First and secondsegment size of 3 seconds, and third and fourth segment sizes of 1second (e.g., a correlation lag value corresponding to a physiologicalrate of 60 BPM) are used to generate paired sorted difference signals11002 and 11004. Paired sorted difference signals 11006 and 11008represent value pairs of first and second sorted difference signals, andvalue pairs third and fourth sorted difference signals, respectively,derived from physiological data exhibiting a dicrotic notch. First andsecond segment size of 3 seconds, and third and fourth segment sizes ofone half a correlation lag value are used to generate paired sorteddifference signals 11006 and 11008. It can be seen that if theprocessing equipment mistakenly posts a half lag, the paired sorteddifference signals for the first and second segments, and the third andfourth segments, may have different characteristics. For example, theendpoints and curve lengths of paired sorted difference signals 11006and 11008 are distinguishable. Paired sorted difference signals 11010and 11012 represent value pairs of first and second sorted differencesignals, and value pairs third and fourth sorted difference signals,respectively, derived from physiological data of a neonate. First andsecond segment size of 3 seconds, and third and fourth segment sizes of1 second (e.g., a correlation lag value corresponding to a physiologicalrate of 60 BPM) are used to generate paired sorted difference signals11010 and 11012. Paired sorted difference signals 11014 and 11016represent value pairs of first and second sorted difference signals, andvalue pairs third and fourth sorted difference signals, respectively,derived from relatively noisier physiological data of a neonate. Firstand second segment size of 3 seconds, and third and fourth segment sizesof one half a correlation lag value are used to generate paired sorteddifference signals 11014 and 11016. It can be seen that if theprocessing equipment mistakenly posts a half lag, the paired sorteddifference signals for the first and second segments, and the third andfourth segments, may have different characteristics. For example, theendpoints and curve lengths of paired sorted difference signals 11014and 11016 are distinguishable.

Step 10830 may include the processing equipment analyzing the first andsecond sorted difference signals to determine one or more first metrics.The one or more first metrics may include a correlation coefficientbetween the first and second sorted difference signals, slopes of thefirst and second sorted difference signals, lengths of the first andsecond sorted difference signals, corresponding values of the first andsecond difference signals, any other suitable metrics, any differencesbetween metrics of the first and second segments thereof, or anycombination thereof.

Step 10832 may include the processing equipment analyzing the third andfourth sorted difference signals to determine one or more secondmetrics. The one or more second metrics may include a correlationcoefficient between the third and fourth sorted difference signals,slopes of the third and fourth sorted difference signals, lengths of thethird and fourth sorted difference signals, corresponding values of thethird and fourth difference signals, any other suitable metrics, anydifferences between metrics of the third and fourth segments thereof, orany combination thereof.

In an illustrative example, the processing equipment may determine anyof the metrics shown in Eqs. 60-67 as the first and second metrics:

$\begin{matrix}{M_{1} = \frac{\overset{N}{\sum\limits_{1}}{\left( {X_{i} - \overset{\_}{X}} \right)\left( {Y_{i} - \overset{\_}{Y}} \right)}}{\sqrt{\overset{N}{\sum\limits_{1}}\left( {X_{i} - \overset{\_}{X}} \right)^{2}}\sqrt{\overset{N}{\sum\limits_{1}}\left( {Y_{i} - \overset{\_}{Y}} \right)^{2}}}} & (60) \\{{M_{2} = {\hat{b}\left( {2,1} \right)}},{{{where}\mspace{14mu}\hat{b}} = {\left( {{\hat{X}}^{T}\hat{X}} \right)^{- 1}\left( {{\hat{X}}^{T}\hat{y}} \right)}}} & (61) \\{M_{3} = {\left( {1 - M_{1}} \right)\left( {1 - M_{2}} \right)}} & (62) \\{M_{4} = {10*\log_{10}M_{3}}} & (63) \\{M_{5} = {\overset{N}{\sum\limits_{1}}\sqrt{{\Delta\; X_{i}^{2}} + {\Delta\; Y_{i}^{2}}}}} & (64) \\{M_{6} = \frac{{M_{2,2} - M_{2,1}}}{M_{2,1}}} & (65) \\{M_{7} = \frac{{M_{5,1} - M_{5,2}}}{M_{5,2}}} & (66) \\{M_{8} = \left\lbrack {{0.5*\left( {X_{1} + Y_{1}} \right)};{0.5*\left( {X_{N} + Y_{N}} \right)}} \right\rbrack} & (67)\end{matrix}$where:X_(i) and Y_(i) are the first and second sorted difference signalvalues, or the third and fourth sorted difference signal values (sortedin ascending order for illustration);ΔX_(i) and ΔY_(i) are difference values of the first and second sorteddifference signal values, or the third and fourth sorted differencesignal values, where ΔX_(i)=X_(i)−X_(i-1), for example;{circumflex over (X)} is a N×2 matrix in which the first column is onesand the second column are the X_(i) values;{circumflex over (X)}^(T) is a transpose of matrix {circumflex over(X)};ŷ is a N×1 matrix of the Y_(i) values;{circumflex over (b)} is a 2×2 matrix;M_(2,2) is M₂ evaluated for the third and fourth sorted differencesignal;M_(2,1) is M₂ evaluated for the first and second sorted differencesignal;M_(5,2) is M₂ evaluated for the third and fourth sorted differencesignal; andM_(5,1) is M₂ evaluated for the first and second sorted differencesignal.

In some embodiments, metric M₁ may be calculated for the first andsecond sorted difference signals, the third and fourth sorted differencesignals, or both. Metric M₁, which is a correlation coefficient, isindicative of how well the sorted difference signals are correlated witheach other. A value near one indicates good correlation, while a valuenear negative one indicates anti-correlation, with values near zeroindicating no correlation. In some embodiments, the processing equipmentmay compare metric M₁ to a threshold as a qualification test.

In some embodiments, metric M₂ may be calculated for the first andsecond sorted difference signals, the third and fourth sorted differencesignals, or both. Metric M₂ may be indicative of the slope of the valuepairs generated from the two segments. A value near one may indicategood correlation, while a value near negative one may indicateanti-correlation. In some embodiments, the processing equipment maycompare metric M₂ to a threshold, or to the same metric generated forthe other two segments, as a qualification test. For pairs of segmentsexhibiting good correlation, metric M₂ values for the first/secondsegments, and third/fourth segments, are expected to be similar.

In some embodiments, metric M₃ may be calculated for the first andsecond sorted difference signals, the third and fourth differencesignals, or both. Metric M₃ may be indicative of the combined slope andcorrelation of the value pairs generated from the two segments. In someembodiments, the processing equipment may compare metric M₃ to athreshold, or to the same metric generated for the other two segments,as a qualification test. For pairs of segment exhibiting goodcorrelation, metric M₃ values for the first/second segments, andthird/fourth segments, are expected to be similar and near zero. This isbecause, for good correlation, the slope and correlation coefficient areexpected to each have a value of near one.

In some embodiments, metric M₄ may be calculated for the first andsecond sorted difference signals, the third and fourth differencesignals, or both. Metric M₄ is a logarithmic scaling of metric M₃, whichmay be indicative of the combined slope and correlation of the valuepairs generated from the two segments.

In some embodiments, metric M₅ may be calculated for the first andsecond sorted difference signals, the third and fourth differencesignals, or both. Metric M₅ is indicative of the length of a particularpaired sorted difference signal generated. For example, value pairs maybe generated from corresponding values of the first and second, or thirdand fourth, sorted difference signals and in a graphical interpretation,metric M₅ represents the length of the resulting curve. In someembodiments, the processing equipment may compare metric M₅ to athreshold, or to the same metric generated for the other two segments,as a qualification test. For pairs of segments exhibiting goodcorrelation, metric M₅ values for the first/second segments, andthird/fourth segments, are expected to be similar. This is because, forgood correlation, the four sorted difference signals are all expected toexhibit a similar shape.

In some embodiments, metric M₆ may be calculated for the first, second,third and fourth difference signals. Metric M₆ is a normalizedcomparison of slope of the value pairs of the first and second segmentswith the slope of the value pairs of the third and fourth segments. Insome embodiments, the processing equipment may compare metric M₆ to athreshold as a qualification test. For pairs of segments exhibiting goodcorrelation, the metric M₆ value is expected to be near zero because theslopes should have similar values near one.

In some embodiments, metric M₇ may be calculated for the first, second,third and fourth difference signals. Metric M₇ is a normalizedcomparison of curve length of the value pairs of the first and secondsegments with the curve length of the value pairs of the third andfourth segments. In some embodiments, the processing equipment maycompare metric M₇ to a threshold as a qualification test. For pairs ofsegments exhibiting good correlation, the metric M₇ value is expected tobe near zero because the curve lengths should be similar.

In some embodiments, metric M₈ may be calculated for the first andsecond sorted difference signals, the third and fourth differencesignals, or both. Metric M₈ includes two values (e.g., averaged endpointcoordinates in a geometrical interpretation), indicative of the endpointvalues of a set of value pairs generated from paired sorted differencesignals. For example, value pairs may be generated from correspondingvalues of the first and second, or third and fourth, sorted differencesignals and in a graphical interpretation, metric M₈ represents how wellthe endpoints of the sorted difference signals agree. In someembodiments, the processing equipment may compare the values of metricM₈ to a threshold, or to the same metric generated for the other twosegments, as a qualification test. For pairs of segments exhibiting goodcorrelation, metric M₈ values for the first/second segments, andthird/fourth segments, are expected to be similar. This is because, forgood correlation, the four sorted difference signals are all expected toexhibit a similar shape with similar endpoints. If any of the segmentsincluded large amounts of noise, for example, the corresponding sorteddifference signal may have more extreme endpoints due to the noise. In afurther example, if the endpoints of the sorted difference signals in apair to not agree with the other pair, the calculated correlation lagvalue may be incorrect (e.g., a shown by paired sorted differencesignals 11014 and 11016 of FIG. 110.

The illustrative metrics shown in Eqs. 60-67 and described above areprovided as examples, and any suitable metrics, or combinations thereof,may be used to analyze two or more segments.

Step 10834 may include processing equipment qualifying or disqualifyingthe correlation lag value of step 10804, based on the first metrics ofstep 10830, the second metrics of step 10832, or both. In someembodiments, the processing equipment may compare a metric value to athreshold to determine whether to qualify or disqualify the correlationlag value. In some embodiments, the processing equipment may comparemetric values to one another to determine whether to qualify ordisqualify the correlation lag value. For example, the processingequipment may determine a percent difference between metric valuescalculated for the first and second sorted differences signals, and thethird and fourth sorted difference signals.

FIG. 111 is a flow diagram 11100 of illustrative steps for qualifying ordisqualifying a value that may be indicative of a physiological ratebased on analyzing harmonic sorted difference signals, in accordancewith some embodiments of the present disclosure. The illustrative stepsof flow diagram 11100 may be referred to as a “Harmonic Rejection Test.”FIGS. 112-114 are discussed in the context of flow diagram 11100.

Step 11102 may include processing equipment receiving physiological datafrom a physiological sensor, memory, any other suitable source, or anycombination thereof. For example, referring to system 300 of FIG. 3, theprocessing equipment may receive a window of physiological data frominput signal generator 310. Sensor 318 of input signal generator 310 maybe coupled to a subject, and may detect physiological activity such as,for example, RED and/or IR light attenuation by tissue, using aphotodetector. In some embodiments, physiological signals generated byinput signal generator 310 may be stored in memory (e.g., RAM 54 of FIG.2, QSM 72 and/or other suitable memory) after being pre-processed bypre-processor 320. In such cases, step 11102 may include recalling datafrom the memory for further processing.

Step 11104 may include the processing equipment receiving a calculatedvalue indicative of a physiological rate of the subject. For example,the calculated value may be based on a correlation lag value (e.g., aperiod associated with a physiological rate), a rate corresponding tothe correlation lag value, or any other calculated value indicative of aphysiological rate of the subject. In some embodiments, step 11104 mayinclude recalling the calculated value from memory for furtherprocessing.

Steps 11106, 11116, 11108, and 11110 may include the processingequipment generating a first difference signal based on the firstsegment of physiological data having a size equal to the correlation lagvalue, a second difference signal based on the second segment ofphysiological data having a size equal to a fraction of the correlationlag value, and a third difference signal based on the third segment ofphysiological data having a size equal to an integer multiple of thecorrelation lag value, respectively. In some embodiments, the processingequipment may perform a subtraction between values of adjacent samplesof a segment to generate the respective difference signal. In someembodiments, the processing equipment may calculate differences bycalculating a first derivative of the respective segment. For example,the processing equipment may compute forward differences, backwarddifferences, or central differences between each pair of adjacent pointsto generate a difference signal. In a further example, the processingequipment may compute a numerical derivative at each point in thesegment, generating a difference signal. Any suitable differencetechnique may be used by the processing equipment to generate thedifference signals. In some embodiments, the fraction of the period ofstep 11108 may be one half, and the integer multiple of step 11110 maybe two.

Steps 11112, 11114, and 11116 may include the processing equipmentsorting the difference values of each difference signal of steps 11106,11108, and 11110, respectively. The processing equipment may sort thevalues in ascending or descending order, either of which causes thenegative and positive values to be separated. Referencing sorted valuesin ascending order, the most negative values come first followed by lessnegative values, positive values, and finally larger positive values.The output of steps 11112, 11114, and 11116 may be a first sorteddifference signal, a second sorted difference signal, and a third sorteddifference signal, respectively.

To illustrate aspects of steps 11102, 11106, 11116, 11108, 11110, 11112,11114, and 11116, FIG. 113 is a panel of illustrative plots showingphysiological data and three selected segments, in accordance with someembodiments of the present disclosure. In some embodiments, theprocessing equipment may select the first, second and third segmentsfrom physiological data 11302 as shown in plot 11300. For example, firstsegment 11304 may be selected as the most recent data having a sizeequal to a calculated correlation lag value (e.g., indicative of aperiod associated with a physiological rate). In a further example,third segment 11308 may be selected as the most recent data having asize equal to twice a calculated correlation lag value (e.g., indicativeof twice a period associated with a physiological rate). Selection ofthe second segment 11306 may include determining, within third segment11308, the segment having a size equal to one half of a calculatedcorrelation lag value (e.g., indicative of half a period associated witha physiological rate) having particular properties. For example, thesegment of size equal to one half of a calculated correlation lag valuehaving a minimum standard deviation may correspond to a relatively flatportion of physiological data (e.g., not substantially including themost extreme difference values), increasing the probability that thesecond segment differs from the first segment in shape. Plot 11310 showsa sorted difference signal corresponding to first segment 11304. Plot11320 shows a sorted difference signal corresponding to second segment11306. Plot 11330 shows a sorted difference signal corresponding tothird segment 11308.

Step 11118 may include the processing equipment analyzing the firstsorted difference signal, the second sorted difference signal, and thethird sorted difference signal to determine one or more first metrics.In some embodiments, the analysis may include determining and comparingshape metrics for the sorted difference signals. Shape metrics mayinclude a best fit slope, end points of a sorted difference signal,length of a sorted difference signal, any other suitable shape orgeometrical metric, or any combination thereof. In some embodiments, theprocessing equipment may perform a KS test, comparing the sorteddifference signal to a predetermined function. In some embodiments, theanalysis may include determining a standard error between any two sorteddifference signals of the three sorted difference signals. For example,FIG. 112 is a panel of illustrative plots showing sorted differencesignals for a lag, half lag, and double lag segment of physiologicaldata, in accordance with some embodiments of the present disclosure.Plots 11200, 11210, and 11220 show three sorted difference signals,corresponding to one lag, one half lag, and double lag segments (e.g.,where the lag is a calculated value), respectively, for which the lag isindicative of a period of a physiological rate. Plots 11230, 11240, and11250 show three sorted difference signals, corresponding to one lag,one half lag, and double lag segments (e.g., where the lag is acalculated value), respectively, for which the lag is indicative ofdouble a period of a physiological rate. Plots 11260, 11270, and 11280show three sorted difference signals, corresponding to one lag, one halflag, and double lag segments (e.g., where the lag is a calculatedvalue), respectively, for which the lag is indicative of one half of aperiod of a physiological rate. Accordingly, the processing equipmentmay distinguish between conditions when a calculated lag value is aharmonic of the lag value associated with a physiological rate. Forexample, when the correct lag value is calculated, the lag and doublelag sorted difference signals are similar in shape, while the half lagsorted difference signal has a different profile because it includesonly a portion of a period of physiological data. In a further example,when double the correct lag value is calculated, the lag, double lag,and half lag sorted difference signals are all similar in shape, becauseall include at least one full period of physiological data. In a furtherexample, when one half of the correct lag value is calculated, the lag,double lag, and half lag sorted difference signals are all different inshape, because they include a half period, a full period, and a quarterperiod, respectively of physiological data.

Step 11120 may include processing equipment qualifying or disqualifyingthe correlation lag value of step 11104, based on the analysis of step11118. For example, if the processing equipment determines that thefirst and second sorted difference signals are not similar, theprocessing equipment may qualify the correlation lag value, while if thefirst and second sorted difference signals are determined to be similar,the processing equipment may disqualify the correlation lag value. In afurther example, if the processing equipment determines that the firstand third sorted difference signals are not similar, the processingequipment may disqualify the correlation lag value, while if the firstand third sorted difference signals are determined to be similar, theprocessing equipment may qualify the correlation lag value.

FIG. 114 is an illustrative plot 11400 showing a contour 11402representing a look-up table for qualifying or disqualifying acorrelation lag value based on analyzing harmonic sorted differencesignals, in accordance with some embodiments of the present disclosure.Contour 11402 shows table output values for input values of a half-lagKS Test metric and the log of a double-lag KS Test metric. The KS Testmay include comparing the respective sorted difference signal to areference distribution, and determining a KS metric value. The KS metricindicates how well the sorted difference signal and referencedistribution match, assuming a value of one for a perfect match and arelatively lower value if the values are less well matched. In someembodiments, the look-up table represented by contour 11402 may begenerated using a neural network classifier technique. For example, thelook-up table may be generated prior to physiological monitoring (e.g.,generated “offline”), based on a reference set of physiological data,and may then be used in lieu of a neural network calculation duringphysiological monitoring. While contour 11402 is indicative of datacalculated using a KS Test, any suitable test may be used to generate alook-up table for qualifying or disqualifying a correlation lag value aspart of the Harmonic Rejection Test.

FIG. 115 is a flow diagram 11500 of illustrative steps for qualifying ordisqualifying a value that may be indicative of a physiological ratebased on a standard deviation ratio (SDR) metric, in accordance withsome embodiments of the present disclosure. The illustrative steps offlow diagram 11500 may be referred to as a “SDR Test.” FIG. 117, whichillustrates a SDR signal, is discussed in the context of flow diagram11500. The SDR test determines a standard deviation, or a metricindicative of standard deviation, and normalized the deviation to abaseline value of the physiological data to generate an SDR metric. TheSDR metric may be compared to an expected range that corresponds tophysiological activity, and accordingly if the SDR metric is outside ofthat range the correlation lag value may be disqualified.

Step 11502 may include processing equipment receiving physiological datafrom a physiological sensor, memory, any other suitable source, or anycombination thereof. For example, referring to system 300 of FIG. 3, theprocessing equipment may receive a window of physiological data frominput signal generator 310. Sensor 318 of input signal generator 310 maybe coupled to a subject, and may detect physiological activity such as,for example, RED and/or IR light attenuation by tissue, using aphotodetector. In some embodiments, physiological signals generated byinput signal generator 310 may be stored in memory (e.g., RAM 54 of FIG.2, QSM 72 and/or other suitable memory) after being pre-processed bypre-processor 320. In such cases, step 11502 may include recalling datafrom the memory for further processing.

Step 11504 may include the processing equipment determining a firstvalue indicative of a baseline of the physiological data. The firstvalue may include a minimum value, mean value, median value, any othersuitable value indicative of a baseline, or any combination thereof. Insome embodiments, the processing equipment may determine a trend-line orother varying baseline, defined for each sample point of thephysiological data. For example, in some embodiments, the baseline maybe a best fit line or a best fit parabola, having a defined valuecorresponding to each sample point. In some embodiments, the processingequipment may subtract the determined baseline from the physiologicaldata, resulting in modified data having a baseline value of zero.

Step 11506 may include the processing equipment determining a secondvalue indicative of a deviation from the baseline of the physiologicaldata determined at step 11504. The second value may include a standarddeviation, an RMS value, a maximum value minus minimum valuecalculation, any other suitable value indicative of deviation from abaseline, or any combination thereof. In some embodiments, theprocessing equipment may perform signal conditioning on thephysiological data prior to determining the second value. For example,the processing equipment may determine the first value of thephysiological data, de-trend the physiological data, and then determinethe second value based on the de-trended physiological data.

Step 11508 may include processing equipment qualifying or disqualifyinga correlation lag value, based on the first value and the second value.In some embodiments, the processing equipment may determine an SDRmetric using an expression such as Eq. 68:

$\begin{matrix}{{SDR} = \frac{S}{M}} & (68)\end{matrix}$where S is a standard deviation value determined at step 11506, and M isa baseline value determined at step 11504, equal to the median value ofthe physiological data, for example. Changes in the SDR value over timemay, for example, indicate a change in the noise level, a change in aphysiological rate of the subject, or both. In some embodiments, theprocessing equipment may determine an SDR metric, using Eq. 68 or someother formulation (e.g., a scaled version), and compare the SDR metricwith a threshold to determine whether to qualify or disqualify thecorrelation lag value.

In an illustrative example, FIG. 116 is a flow diagram of illustrativesteps for implementing a standard deviation ratio (SDR) technique, inaccordance with some embodiments of the present disclosure. Step 11602may include determining an SDR metric value at current time T based onphysiological data. Step 11604 may include comparing the SDR metricvalue to a threshold X, which may be any suitable value (e.g., X may be0.28 in some embodiments). If the SDR metric value exceeds thethreshold, the processing equipment may disqualify the currentlycalculated correlation lag value at step 11614. If the SDR metric valuedoes not exceed the threshold, the processing equipment may then comparethe SDR metric value with the minimum SDR metric value of a set ofprevious SDR values at step 11606. As illustrated, the SDR metric iscompared with five times the minimum SDR metric value in a time intervalfrom T₁ to T₂, which may be, for example, the last 30 seconds worth ofcalculated SDR values stored in a buffer 11612. This threshold isexemplary. The threshold may be any suitable fixed or variable value.Typically, the SDR metric is not expected to increase significantly overrelatively short time scales (e.g., 30 seconds). The presence of noisetypically increases the SDR metric value. Accordingly, by comparing thecurrent SDR metric with the minimum SDR metric value from the history ofvalues, the processing equipment may determine if the currentphysiological data is relatively noisy. If the SDR metric value exceedsthe threshold of step 11606, the processing equipment may disqualify thecurrently calculated correlation lag value at step 11614. If the SDRmetric value does not exceed the threshold of step 11606, the processingequipment may qualify the SDR metric value at step 11608. Whetherqualified or disqualified, the current SDR metric value is added tobuffer 11612 at step 11610, and the processing equipment may return tostep 11602.

FIG. 117 is a panel of illustrative plots showing a physiologicalsignal, an SDR signal, an SDR threshold, and a test outcome signal, inaccordance with some embodiments of the present disclosure. The abscissaof plots 11700, 11710, and 11720 are in units of sample number, whilethe ordinates are shown in arbitrary units. Plot 11700 shows IRintensity signal 11702. Plot 11710 shows a calculated SDR signal 11712,and a threshold signal 11714 calculated as five times the minimum SDRvalue of the previous 30 seconds of IR intensity signal 11702. Plot11720 shows SDR test outcome signal 11722 which assumes a value of onewhen SDR signal 11712 does not exceed threshold signal 11714, and avalue of zero when SDR signal 11712 exceeds threshold signal 11714.Accordingly, the presence of significant noise (e.g., illustrated by IRintensity signal 11702 between abscissa values of 25000 and 30000 inplot 11700) may cause the SDR Test to fail.

FIG. 118 is a flow diagram of illustrative steps for qualifying ordisqualifying a value that may be indicative of a physiological ratebased on a different standard deviation ratio (SDR_(II)) metric, inaccordance with some embodiments of the present disclosure. Theillustrative steps of flow diagram 11800 may be referred to as a “SDR IITest.” In some instances, physiological data may include multiplecomponents, which may include the desired physiological component and anundesired component. The SDR_(II) metric may be indicative of signalenergy before and after signal conditioning (e.g., a ratio of standarddeviation values before and after conditioning), which may be indicativeof the amount of signal content removed during conditioning. Forexample, if the SDR_(II) metric indicates that a large percentage ofsignal energy was removed during signal conditioning, then theprocessing equipment may determine the remaining signal energy is likelynoise. If the level of noise in the physiological data is relativelylow, the SDR_(II) metric is expected to be within a particular range. Insome embodiments, the SDR_(II) metric may be compared to a threshold.For example, if the metric exceeds the threshold, the processingequipment may disqualify the correlation lag value, and if the metricdoes not exceed the threshold, the processing equipment may qualify thecorrelation lag value.

Step 11802 may include processing equipment receiving physiological datafrom a physiological sensor, memory, any other suitable source, or anycombination thereof. For example, referring to system 300 of FIG. 3, theprocessing equipment may receive a window of physiological data frominput signal generator 310. Sensor 318 of input signal generator 310 maybe coupled to a subject, and may detect physiological activity such as,for example, RED and/or IR light attenuation by tissue, using aphotodetector. In some embodiments, physiological signals generated byinput signal generator 310 may be stored in memory (e.g., RAM 54 of FIG.2, QSM 72 and/or other suitable memory) after being pre-processed bypre-processor 320. In such cases, step 11802 may include recalling datafrom the memory for further processing.

Step 11804 may include the processing equipment determining a firstvalue indicative of a deviation from a baseline of the physiologicaldata determined at step 11802. The first value may include a standarddeviation, an RMS value, any other suitable value indicative ofdeviation from a baseline, or any combination thereof.

Step 11806 may include the processing equipment performing signalconditioning on the physiological data of step 11802. Any suitableSignal Conditioning Technique may be applied to the physiological datasuch as, for example, application of a bandpass filter, de-trending, orother techniques. The output of step 11806 may be conditioned data,which may be stored in a buffer or other memory allocation.

Step 11808 may include the processing equipment determining a secondvalue indicative of a deviation from a baseline of the conditioned dataof step 11806. The second value may include a standard deviation, an RMSvalue, any other suitable value indicative of a deviation from abaseline in the conditioned data, or any combination thereof.

Step 11810 may include processing equipment qualifying or disqualifyinga correlation lag value, based on the first value and the second value.In some embodiments, the processing equipment may determine an SDR_(II)metric using an expression such as Eq. 69:

$\begin{matrix}{{SDR}_{II} = \frac{S_{1}}{S_{2}}} & (69)\end{matrix}$where S₁ is a standard deviation value determined at step 11804, and S₂is a standard deviation value determined at step 11808, for example.Changes in the SDR_(II) value over time may, for example, indicate achange in the noise level, a change in a physiological rate of thesubject, or both. In some embodiments, the processing equipment maydetermine an SDR_(II) metric, using Eq. 69 or some other formulation,and compare the SDR_(II) metric with a threshold to determine whether toqualify or disqualify the correlation lag value.

FIG. 119 is a flow diagram 11900 of illustrative steps for qualifying ordisqualifying a value that may be indicative of a physiological ratebased on a statistical metric such as a p-value, in accordance with someembodiments of the present disclosure. The illustrative steps of flowdiagram 11900 may be referred to as the “p-Value Test.” The statisticalmetric may be indicative of the probability that a property of a set ofvalue pairs would be obtained due to chance.

Step 11902 may include processing equipment receiving physiological datafrom a physiological sensor, memory, any other suitable source, or anycombination thereof. For example, referring to system 300 of FIG. 3, theprocessing equipment may receive a window of physiological data frominput signal generator 310. Sensor 318 of input signal generator 310 maybe coupled to a subject, and may detect physiological activity such as,for example, RED and/or IR light attenuation by tissue, using aphotodetector. In some embodiments, physiological signals generated byinput signal generator 310 may be stored in memory (e.g., RAM 54 of FIG.2, QSM 72 and/or other suitable memory) after being pre-processed bypre-processor 320. In such cases, step 11902 may include recalling datafrom the memory for further processing.

Step 11904 may include the processing equipment receiving a calculatedvalue indicative of a potential physiological rate of the subject. Forexample, the calculated value may be based on a correlation lag value, arate corresponding to the correlation lag value, or any other calculatedvalue indicative of a physiological rate of the subject. In someembodiments, step 11904 may include recalling the calculated value frommemory for further processing.

Step 11906 may include the processing equipment generating pairs ofsample points of the physiological data, spaced apart in thephysiological data. The spacing may be based on the calculated valueindicative of a physiological rate of the subject. For example, thecalculated value may be based on a correlation lag value, a ratecorresponding to the correlation lag value, or any other calculatedvalue indicative of a physiological rate of the subject. In someembodiments, as illustrated by panel 11950, a first portion ofphysiological data 11952, including a particular number of samplepoints, may be point-wise paired with a second portion of thephysiological data 11954, including the same number of samples points asthe first portion albeit shifted in time relative to the first portion.In some embodiments, generating the pairs of points may includespecifying indices of the points in the physiological data forreference.

Step 11908 may include the processing equipment determining a best fitline based on the pairs of points of step 11906. In some embodiments,the processing equipment may apply a linear regression to the pairs ofpoints. In some embodiments, the processing equipment may apply aconstrained best line fit to the pairs of points such as, for example,constraining the best fit line to include the point (0,0). Panel 11960graphically illustrates pairs of points 11962, and corresponding bestfit line 11964. In some embodiments, the output of step 11908 may be aslope value (e.g., corresponding to a line including point 0,0), or anintercept and slope value (e.g., corresponding to a y=mx+b formulation).

Step 11910 may include the processing equipment determining astatistical metric based on the best fit line and the pairs of points.In some embodiments, the processing equipment may use an expression suchas that shown in Eq. 70:

$\begin{matrix}{{SE} = \frac{\sqrt{\frac{1}{N - 2}{\sum\left( {y_{i} - {\hat{y}}_{i}} \right)^{2}}}}{\sqrt{\sum\left( {x_{i} - \overset{\_}{x}} \right)^{2}}}} & (70)\end{matrix}$to determine a standard error value SE, where N is the number of pairs,x_(i) are the first values of the pairs, y_(i) are the second values ofthe pairs, {circumflex over (x)} is the average of the first values, andŷ_(i) is the best fit line value corresponding to x_(i). In someembodiments, the processing equipment may determine a t-statistic metrict based on the standard error SE and the best fit line using Eq. 71:

$\begin{matrix}{t = \frac{m}{SE}} & (71)\end{matrix}$where m is the slope of the best fit line of step 11908. The processingequipment may then access a look-up table of one-sided or two-sidedprobabilities that a t-statistic greater than or equal to the determinedvalue would be obtained due to chance (e.g., the p-value). Toillustrate, panel 11970 shows an illustrative probability distribution11972, with regions 11974 and 11976 indicating the two-sidedprobabilities of more extreme t-statistic values. The degrees of freedomof the calculation may be calculated as N−1, and a standard look-uptable of Student-t distribution probabilities may be used. In someembodiments, the processing equipment may calculate probabilities (e.g.,p-values) based on t-statistics for a series of correlation lag values.For example, the processing equipment may calculate probabilities forthe correlation lag value indicative of the physiological rate, and thethree adjacent smaller lags values, and three adjacent larger lagvalues.

Step 11912 may include processing equipment qualifying or disqualifyingthe calculated value of step 11904, based on the determined statisticalmetric of step 11910. In some embodiments, the processing equipment mayuse an inequality as a qualifying or a disqualifying criterion. Forexample, the processing equipment may calculate probabilities for thecorrelation lag value indicative of the physiological rate, and thethree adjacent smaller lags values, and three adjacent larger lagvalues. Further, the processing equipment may use an expression such asEq. 72:

$\begin{matrix}{{\overset{3}{\sum\limits_{- 3}}{p\left( {l + i} \right)}} < {4*0.05}} & (72)\end{matrix}$where l is the correlation lag value indicative of a physiological rate,i is an index, and p(l+i) is the probability value determined based onEq. 71 and the look-up table of p-values for the indexed correlation lagvalue l+i. If the inequality of Eq. 72 is true, the processing equipmentmay qualify the correlation lag value of step 11904, while if theinequality is false, the processing equipment may disqualify thecorrelation lag value of step 11904. In a further example, theprocessing equipment may determine a p-value for the correlation lagvalue of step 11904, and compare the p-value with a threshold. If thep-value exceeds the threshold, the processing equipment may disqualifythe correlation lag value, and if the p-value does not exceed thethreshold, the processing equipment may qualify the correlation lagvalue.

FIG. 120 is a flow diagram of illustrative steps for qualifying ordisqualifying a value that may be indicative of a physiological ratebased on a maximum and minimum of a correlation sequence, in accordancewith some embodiments of the present disclosure. The illustrative stepsof flow diagram 12000 may be referred to as a “Correlation Max-MinTest.”

Step 12002 may include processing equipment receiving physiological datafrom a physiological sensor, memory, any other suitable source, or anycombination thereof. For example, referring to system 300 of FIG. 3, theprocessing equipment may receive a window of physiological data frominput signal generator 310. Sensor 318 of input signal generator 310 maybe coupled to a subject, and may detect physiological activity such as,for example, RED and/or IR light attenuation by tissue, using aphotodetector. In some embodiments, physiological signals generated byinput signal generator 310 may be stored in memory (e.g., RAM 54 of FIG.2, QSM 72 and/or other suitable memory) after being pre-processed bypre-processor 320. In such cases, step 12002 may include recalling datafrom the memory for further processing.

Step 12004 may include processing equipment generating a correlationsequence based on the physiological data. In some embodiments, theprocessing equipment may generate the correlation sequence bymultiplying corresponding values of two segments of the physiologicaldata at a particular lag, for a sequence of lag values. Step 12004 mayinclude the processing equipment normalizing the physiological data ofstep 12002. In some embodiments, the processing equipment may use anysuitable signal conditioning technique (e.g., de-trending and/ornormalization techniques, scaling, shifting, or any other suitableoperation) to condition the physiological data before generating thecorrelation sequence.

Step 12006 may include processing equipment identifying a correlationlag value of a maximum in the correlation sequence, corresponding to apeak in the correlation sequence of step 12004. The identifiedcorrelation lag value may be identified using any of the illustrativetechniques described in FIGS. 66, 67, 68, and 70. In some embodiments,step 12006 may include generating the threshold. The threshold may begenerated using a predetermined value, a predetermined function, a valuebased on a previously calculated rate, a value based on the currentoperating Mode, a value based one or more metrics derived from thephysiological data (e.g., de-trending metrics, noise metrics), using anyother suitable technique, or any combination thereof. The processingequipment may identify threshold crossings by comparing all or some ofthe correlation output to the threshold. The processing equipment mayuse any suitable peak finding techniques to identify the peak such as,for example, identifying a maximum, identifying an upstroke (i.e.,positive slope) and downstroke (i.e., negative slope), applying athreshold, comparing one or more peaks to identify a particular peak(e.g., a largest peak, a peak occurring first in terms of lag), anyother suitable peak finding technique, or any combination thereof. Theprocessing equipment may identify the correlation lag value of themaximum value, lag values associated with the peak, or a combinationthereof.

Step 12008 may include processing equipment identifying a relativelylesser lag value of a minimum in the correlation sequence, correspondingto a trough in the correlation sequence of step 12004. In someembodiments, the minimum corresponds to the nearest adjacent troughhaving a relatively lesser lag value than the peak. For example,referencing plot 6500 of FIG. 65, the processing equipment may identifythe maximum associated with the peak located at a lag value ofapproximately 70 in units of sample point, and identify the minimumassociated with the trough at a lag value of approximately 35 in unitsof sample point. In some embodiments, the processing equipment may onlyidentify a lesser lag value at least a particular number of samples fromthe maximum value. For example, the processing equipment may onlyconsider lesser lag values at least 10 samples before the lag valuecorresponding to the maximum. In a further example, the particularnumber of samples may be a function of a calculated physiological rateor a calculated correlation lag value (e.g., at larger calculatedcorrelation lag values, the number of samples between the min and maxmay be larger).

Step 12010 may include processing equipment qualifying or disqualifyingthe correlation lag value of step 12006, based on the maximum andminimum of steps 12006 and 12008, respectively. In some embodiments, theprocessing equipment may determine a difference between the maximumcorrelation value of step 12006 and the minimum correlation value fromstep 12008 in order to determine whether to qualify the correlation lagvalue. For example, the processing equipment may determine thedifference between a maximum correlation value and a minimum correlationvalue, and compare the difference to a threshold. If the difference isabove the threshold, the processing equipment may qualify thecorrelation lag value associated with the maximum. If the normalizeddifference is below the threshold, the processing equipment maydisqualify the correlation lag value associated with the maximum.

FIG. 121 is a flow diagram 12100 of illustrative steps for adjusting aqualification or disqualification criterion based on noise, inaccordance with some embodiments of the present disclosure.

Step 12102 may include processing equipment receiving physiological datafrom a physiological sensor, memory, any other suitable source, or anycombination thereof. For example, referring to system 300 of FIG. 3, theprocessing equipment may receive a window of physiological data frominput signal generator 310. Sensor 318 of input signal generator 310 maybe coupled to a subject, and may detect physiological activity such as,for example, RED and/or IR light attenuation by tissue, using aphotodetector. In some embodiments, physiological signals generated byinput signal generator 310 may be stored in memory (e.g., RAM 54 of FIG.2, QSM 72 and/or other suitable memory) after being pre-processed bypre-processor 320. In such cases, step 12102 may include recalling datafrom the memory for further processing.

Step 12104 may include the processing equipment receiving a calculatedvalue indicative of a potential physiological rate of the subject. Forexample, the calculated value may be based on a correlation lag value, arate corresponding to the correlation lag value, or any other calculatedvalue indicative of a physiological rate of the subject. In someembodiments, step 12104 may include recalling the calculated value frommemory for further processing.

Step 12106 may include processing equipment determining a valueindicative of noise (e.g., a noise metric) using any of the techniquesdisclosed herein, or any combination thereof. For example, theprocessing equipment may determine a metric value using any of thetechniques discussed in the context of FIGS. 11-41. In some embodiments,the processing equipment may determine multiple noise metric values, andthen select a single value, generate a combined value using a suitabletechnique (e.g., an average, a weighted average, a product, or someother combination), determine a noise metric based on a lookup tableusing one or more noise metrics as an input, perform any other suitablecalculation of a noise metric, or any combination thereof.

Step 12108 may include processing equipment adjusting at least onequalification criterion based on the value indicative of noise of step12106. Adjusting the at least one criterion may include, for example,adjusting a threshold used in a qualification test, adjusting a metriccalculated in a qualification test, adjusting a setting of aqualification test, adjusting which qualification test or combination oftests are used, or any combination thereof. For example, in someembodiments, the processing equipment may loosen one or more thresholdsto lessen the probability of disqualification under relatively noisyconditions. Further, in some embodiments, the processing equipment maytighten one or more thresholds to increase the probability ofdisqualification under relatively less noisy conditions. Accordingly,when physiological data exhibits less noise, the processing equipmentmay apply more strict qualification tests, or settings of tests, toincrease the confidence in qualified values.

Step 12110 may include processing equipment qualifying or disqualifyingthe calculated value of step 12104, based on the adjusted qualificationcriterion of step 12108. In some embodiments, the processing equipmentmay apply the adjusted qualification criterion to the physiological datato determine whether to qualify or disqualify the calculated value.

FIG. 122 is a flow diagram 12200 of illustrative steps for adjusting aqualification or disqualification criterion based on a value indicativeof a physiological rate, in accordance with some embodiments of thepresent disclosure.

Step 12202 may include processing equipment receiving physiological datafrom a physiological sensor, memory, any other suitable source, or anycombination thereof. For example, referring to system 300 of FIG. 3, theprocessing equipment may receive a window of physiological data frominput signal generator 310. Sensor 318 of input signal generator 310 maybe coupled to a subject, and may detect physiological activity such as,for example, RED and/or IR light attenuation by tissue, using aphotodetector. In some embodiments, physiological signals generated byinput signal generator 310 may be stored in memory (e.g., RAM 54 of FIG.2, QSM 72 and/or other suitable memory) after being pre-processed bypre-processor 320. In such cases, step 12202 may include recalling datafrom the memory for further processing.

Step 12204 may include the processing equipment receiving a calculatedvalue indicative of a potential physiological rate of the subject. Forexample, the calculated value may be based on a correlation lag value, arate corresponding to the correlation lag value, or any other calculatedvalue indicative of a physiological rate of the subject. In someembodiments, step 12204 may include recalling the calculated value frommemory for further processing.

Step 12206 may include processing equipment adjusting at least onequalification criterion based on the value indicative of a physiologicalrate of step 12206. Adjusting the at least one criterion may include,for example, adjusting a threshold used in a qualification test,adjusting a metric calculated in a qualification test, adjusting asetting of a qualification test, adjusting which qualification test orcombination of tests are used, or any combination thereof. In someembodiments, for example, the processing equipment may determine not toperform qualification tests that are directed to identifying a lock on adouble rate when the calculated rate is lower than a predeterminedvalue. For example, at calculated rates of 50 BPM or less, theprocessing equipment may refrain from perform qualifications tests thatindicate a double rate lock condition. In a further example, when therate is relatively low (e.g., 40 BPM or less), the processing equipmentmay perform qualification tests that indicate whether the rate algorithmis tracking low frequency noise.

Step 12208 may include processing equipment qualifying or disqualifyingthe calculated value of step 12204, based on the adjusted qualificationcriterion of step 12208. In some embodiments, the processing equipmentmay apply the adjusted qualification criterion to the physiological datato determine whether to qualify or disqualify the calculated value.

FIG. 123A is a flow diagram 12300 of illustrative steps for combiningqualification tests, in accordance with some embodiments of the presentdisclosure.

Step 12302 may include processing equipment receiving physiological datafrom a physiological sensor, memory, any other suitable source, or anycombination thereof. For example, referring to system 300 of FIG. 3, theprocessing equipment may receive a window of physiological data frominput signal generator 310. Sensor 318 of input signal generator 310 maybe coupled to a subject, and may detect physiological activity such as,for example, RED and/or IR light attenuation by tissue, using aphotodetector. In some embodiments, physiological signals generated byinput signal generator 310 may be stored in memory (e.g., RAM 54 of FIG.2, QSM 72 and/or other suitable memory) after being pre-processed bypre-processor 320. In such cases, step 12302 may include recalling datafrom the memory for further processing. Step 12302 may includeprocessing equipment receiving qualification information such as acalculated correlation lag value, a calculated rate, any suitablealgorithm settings used for qualification, any other suitableinformation, or any combination thereof.

Steps 12304, 12306, and 12308 may include processing equipmentperforming a first qualification test, a second qualification test,through an N^(th) qualification test, respectively to qualify ordisqualify a calculated correlation lag value based on the physiologicaldata and the qualification information. Any suitable number ofqualification tests may be performed, for example, using any of theillustrative Qualification Techniques discussed in the context of FIGS.86-122. The qualification tests may be performed in parallel, in series,or a combination thereof.

Step 12310 may include processing equipment qualifying or disqualifyingthe correlation lag value based on the qualification tests of steps12304, 12306, and 12308. In some embodiments, the processing equipmentmay average, sum, or otherwise combine one or more qualification metricsfrom the qualification tests. In some embodiments, the processingequipment may qualify the correlation lag value if all of thequalification tests are passed. In some embodiments, the processingequipment may qualify the correlation lag value if at least a particularpercentage of the qualification tests are passed. For example,referencing flow diagram 12300, the processing equipment maysequentially apply qualification tests and disqualify the correlationlag value when a qualification test fails. In a further example, theprocessing equipment may pass the results of the qualification tests toa neural network calculation, to determine one or more output values(e.g., qualification pass or fail), as described below in the context ofFIG. 123B.

FIG. 123B is a block diagram of an illustrative neural network 12350that may receive a combination of inputs, in accordance with someembodiments of the present disclosure. Neural network 12350 is aninterconnected group of nodes graphically representing a calculationtechnique. In some embodiments, neural network 12350 may be trainedusing a set of training data to determine coefficients or otherparameters, and then apply the coefficient or other parameters tocurrent physiological data. In some embodiments, neural network 12350may be adaptive, where the coefficients or other parameters are updated(e.g., neural network learning) as new physiological data is analyzed.For example, in some circumstances an electrocardiographic (EKG) probemay be used to provide accurate rate information of a subject, and theprocessing equipment may further train an adaptive neural network usingrecent physiological data and the EKG probe information. Theinterconnections between the nodes represent information flows. Forexample, the processing equipment may perform one or more qualificationtests, using the same physiological data, as described in flow diagram12300 of FIG. 123A. The results of each, such as a metric value (e.g., anumber) or pass/fail value (e.g., zero or one, “pass” or “fail”), forexample, may be used as inputs to input nodes 12352, 12354, and 12356test (e.g., N input nodes where N is any suitable integer greater thanor equal to two) of neural network 12350. In some embodiments, one ormore metrics, such as those described in the context of FIGS. 11-41, mayalso be used as an input. In some embodiments, a calculated correlationlag value may also be used as an input. The input values (e.g., metricvalues or pass/fail values) may be passed to hidden nodes 12362, 12364,and 12366 (e.g., M hidden nodes where M is any suitable integer greaterthan or equal to two) of neural network 12350. The processing equipmentmay perform calculations at hidden nodes 12362, 12364, and 12366 on theinput values from input nodes 12352, 12354, and 12356, usingpredetermined functions or other predetermined calculations. The outputsfrom hidden nodes 12362, 12364, and 12366 may be passed to output node12372. The processing equipment may perform calculations at output node12372 on the outputs from hidden nodes 12362, 12364, and 12366, usingpredetermined functions or other predetermined calculations. Output node12372 may output information that may be used to qualify or disqualify avalue that may be indicative of a physiological rate. For example, theoutput information may be a number (e.g., a number between 0 and 1,where a number greater than or equal to 0.5 may cause the value to bequalified), a text string such as “pass” or “fail,” or both. In someembodiments, the number between 0 and 1 may be indicative of theprobability or confidence that the value correctly indicates thephysiological rate. For example, a low number may indicate lowconfidence and a high number may indicate high confidence. Theprocessing equipment may qualify or disqualify a value, such as acorrelation lag value, based on the output information of neural network12350. For example, the processing equipment may compare the number to athreshold to determine whether or not to qualify the value

FIG. 124 is a flow diagram 12400 of illustrative steps for combiningqualification tests, in accordance with some embodiments of the presentdisclosure.

Step 12402 may include processing equipment receiving physiological datafrom a physiological sensor, memory, any other suitable source, or anycombination thereof. For example, referring to system 300 of FIG. 3, theprocessing equipment may receive a window of physiological data frominput signal generator 310. Sensor 318 of input signal generator 310 maybe coupled to a subject, and may detect physiological activity such as,for example, RED and/or IR light attenuation by tissue, using aphotodetector. In some embodiments, physiological signals generated byinput signal generator 310 may be stored in memory (e.g., RAM 54 of FIG.2, QSM 72 and/or other suitable memory) after being pre-processed bypre-processor 320. In such cases, step 12402 may include recalling datafrom the memory for further processing. Step 12402 may includeprocessing equipment receiving qualification information such as acalculated correlation lag value, a calculated rate, any suitablealgorithm settings used for qualification, any other suitableinformation, or any combination thereof.

Steps 12404, 12406, and 12408 may include processing equipmentperforming a first qualification test, a second qualification test,through an N^(th) qualification test, in sequence to qualify ordisqualify a calculated correlation lag value based on the physiologicaldata and the qualification information. Any suitable number ofqualification tests may be performed, for example, using any of theillustrative Qualification Techniques discussed in the context of FIGS.86-122. The qualification tests may be performed in series, for example,and if a disqualification occurs at any qualification test, theprocessing equipment may skip any remaining qualification tests andproceed to disqualification at step 12410.

Step 12410 may include processing equipment qualifying or disqualifyingthe correlation lag value based on the qualification tests of steps12404, 12406, and 12408. In some embodiments, the processing equipmentmay qualify the correlation lag value if all of the qualification testsare passed. For example, referencing flow diagram 12400, the processingequipment may sequentially apply qualification tests and disqualify thecorrelation lag value when a qualification test fails.

In some embodiments, the illustrative steps of flow diagrams 12500 and12600 of FIGS. 125 and 126, respectively, may be performed to qualify ordisqualify one or more correlation lag values based on across-correlation calculation using a cross-correlation template. Insome embodiments, the illustrative analyses of flow diagrams 12500 and12600 of FIGS. 125 and 126, respectively, may be performed to increaseconfidence in a qualified rate. In some embodiments, flow diagrams 12500and 12600 of FIGS. 125 and 126, respectively, may use a combination ofany or all of the previously discussed qualification techniques. Forexample, the steps of flow diagrams 12500 and 12600 of FIGS. 125 and 126may be performed to further investigate whether correlation lag valuescorrespond to a physiological rate of interest (e.g., pulse rate) byusing multiple types of templates, multiple lengths of templates, orboth.

FIG. 125 is a flow diagram 12500 of illustrative steps for analyzingqualification metrics based on scaled templates of different lengths, inaccordance with some embodiments of the present disclosure. In someembodiments, performance of the illustrative steps of flow diagram 12500may provide an evaluation of the qualification, or disqualification, ofa correlation lag value. The illustrative steps of flow diagram 12500may be referred to as “Qualification Analysis.”

Step 12502 may include processing equipment receiving a correlation lagvalue, any other suitable qualification information, or any combinationthereof. In some embodiments, step 12502 may include recalling thestored correlation lag value from suitable memory. In some embodiments,a single processor, module, or system may calculate, store, or both, thecorrelation lag value and perform steps 12504-12508, and accordingly,step 12502 need not be performed.

Step 12504 may include the processing equipment computing one or morequalification metrics based on a template scaled to a first length. Insome embodiments, the scaled template may be used to generate across-correlation signal (e.g., using any suitable steps of flow diagram8600 of FIG. 86), which may be analyzed according to any of theQualification Techniques. In some embodiments, step 12504 may includeperforming a Symmetry Test, performing a Radius Test, performing anAngle Test, performing an Area Test, performing an Area Similarity Test,performing a Statistical Property Test, performing a High FrequencyResidual Test, performing any other suitable test, performing anyportions thereof, or any combination thereof. For example, aqualification metric may include a variability metric (e.g., calculatedusing Eq. 47 or 48), a radius value (e.g., calculated using Eq. 49), andangle sum value (e.g., calculated using Eq. 55), an area of a segment, astatistical property, any other suitable metric, any metric derivedthereof, or any combination thereof.

Step 12506 may include the processing equipment computing one or morequalification metrics based on a template scaled to a particular lengthdifferent than the length of step 12504. In some embodiments, step 12506may include performing a Symmetry Test, performing a Radius Test,performing an Angle Test, performing an Area Test, performing an AreaSimilarity Test, performing a Statistical Property Test, performing aHigh Frequency Residual Test, performing any other suitable test,performing any portions thereof, or any combination thereof. Step 12506may be performed any suitable number of times with templates ofdifferent lengths, as indicated by the ellipsis of flow diagram 12500.

In some embodiments, using templates scaled to different lengths atsteps 12504-12506 may aid in determining whether the correlation lagvalue is the true physiological rate. In some embodiments, a templatemay be scaled to a first length, based on the correlation lag value orassociated period (e.g., scaled to one half, one third, or double therate). In some embodiments, the scaling used may be based on thecorrelation lag value. In a further example, a template may be scaledbased on expected noise and physiological ranges. For example, if thecalculated rate is 50 BPM, a template need not be scaled to thehalf-rate because 25 BPM is an unlikely physiological pulse rate. Forrelatively lower rates, templates scaled to higher rates may be used todetermine if the correlation lag value is indicative of low-frequencynoise. In a further example, for higher rates (e.g., 90 BPM and above) atemplate may be scaled to one half, one third, or other fraction of thecalculated rate to determine if the correlation lag value corresponds toa harmonic of the physiological rate.

Step 12508 may include the processing equipment analyzing thequalification metrics of steps 12504 and 12506 to determine whether toqualify or disqualify the correlation lag value of step 12502. Forexample, the Angle Test may give a calculated angle sum of about 700°when a template is scaled to the calculated rate. If the Angle Testgives a calculated angle sum of about 360° when a template is scaled toone half of the period corresponding to the calculated rate, then theprocessing equipment may determine that there is a half-rate condition,and accordingly may qualify the half-rate and/or disqualify thecorrelation lag value. In a further example, two templates of the samelength may be used at steps 12504 and 12506, one having a dicrotic notchand the other having no dicrotic notch. If the processing equipmentqualifies at least one of the two templates, then correlation lag valuemay be qualified.

FIG. 126 is a flow diagram 12600 of illustrative steps for selecting oneor more templates, and analyzing qualification metrics based on scaledtemplates, in accordance with some embodiments of the presentdisclosure. In some embodiments, performance of the illustrative stepsof flow diagram 12600 may provide an evaluation of the qualification, ordisqualification, of one or more correlation lag values.

Step 12602 may include processing equipment receiving a correlation lagvalue, any other suitable qualification information, or any combinationthereof. In some embodiments, step 12602 may include recalling thestored correlation lag value from suitable memory. In some embodiments,a single processor, module, or system may calculate, store, or both, thecorrelation lag value and perform steps 12604-12610, and accordingly,step 12602 need not be performed.

Step 12604 may include the processing equipment selecting one or moretemplates based on the qualification information of step 12602. In someembodiments, a variety of template shapes may be available to theprocessing equipment (e.g., stored in suitable memory accessible to theprocessing equipment). For example, some templates may exhibit adicrotic notch while others do not exhibit a dicrotic notch. In afurther example, some templates may exhibit symmetric peaks, whileothers do not exhibit symmetric peaks. In some embodiments, the templatetype may depend on a representative rate or period, which may beassociated with a correlation lag value. For example, in someembodiments, the processing equipment may select the one or moretemplates depending on the value of period P. For example, at relativelylower values of period P, a relatively more symmetrical template,without a dicrotic notch, may be selected. In some embodiments, apredefined number of templates may be used (e.g., all templates arealways used), and accordingly, the selection of step 12604 need not beperformed.

Step 12606 may include the processing equipment scaling a firsttemplate, of the one or more templates of step 12604, and calculatingone or more qualification metrics based on the scaled first template. Insome embodiments, the scaled first template may be used to generate across-correlation signal (e.g., using any suitable steps of flow diagram8600 of FIG. 86), which may be analyzed according to any of theQualification Techniques. In some embodiments, step 12606 may includeperforming a Symmetry Test, performing a Radius Test, performing anAngle Test, performing an Area Test, performing an Area Similarity Test,performing a Statistical Property Test, performing a High FrequencyResidual Test, performing any other suitable test, performing anyportions thereof, or any combination thereof.

Step 12608 may include the processing equipment scaling a secondtemplate, of the one or more templates of step 12604, and calculatingone or more qualification metrics based on the scaled second template.In some embodiments, the scaled second template may be used to generatea cross-correlation signal (e.g., using any suitable steps of flowdiagram 8600 of FIG. 86), which may be analyzed according to any of theQualification Techniques. In some embodiments, step 12608 may includeperforming a Symmetry Test, performing a Radius Test, performing anAngle Test, performing an Area Test, performing an Area Similarity Test,performing a Statistical Property Test, performing a High FrequencyResidual Test, performing any other suitable test, performing anyportions thereof, or any combination thereof. Step 12608 may beperformed any suitable number of times with different template shapes,as indicated by the ellipsis of flow diagram 12600.

Step 12610 may include the processing equipment analyzing thequantification metrics of steps 12606 and 12608 to determine whether toqualify or disqualify a correlation lag value. The use of templateshaving different shapes may allow for more confidence in the results ofa qualification. For example, if a calculated rate is 120 BPM or higher,then a symmetric template may be used. Further, if the calculated rateis below 120 BPM, then four templates may be used (e.g., symmetric andasymmetric, both with and without a dicrotic notch) to determine whichtemplate provides the best results during qualification.

In some embodiments, any or all of the illustrative steps of flowdiagrams 12500 and 12600 of FIGS. 125 and 126 may be suitably combined.The illustrative steps of flow diagrams 12500 and 12600 may be performedsequentially, simultaneously, alternately, or any other suitablecombination. In some embodiments, variation in both the length and shapeof a template may be used to aid in qualifying or disqualifying acorrelation lag value. In some embodiments, the shape of a template maydepend on the length of the template. For example, templates of shorterlength (i.e., shorter period), may exhibit more symmetry than templatesof longer length. In some embodiments, a set of templates may be used,which each may have a particular length and shape. For example, a coarseset of templates (e.g., spanning a relatively large range of lengths,shapes, or both) may be used initially to determine a region of interestamong the lengths and shapes (e.g., which templates provide the bestresult). A more refined set of templates, selected based on the regionof interest, may then be used to calculate the rate with more accuracy,confidence, or both.

In some embodiments, the processing equipment may use data interpolationto implement any of the Qualification Techniques disclosed herein (e.g.,discussed in the context of FIGS. 86-126). At higher physiologicalrates, a single period of physiological data (e.g., a segment of datahaving a size equal to a correlation lag value corresponding to anidentified peak) may include relatively less data points. For example,at a physiological rate of 240 BPM, the associated period is 0.25seconds. For a sample rate of 57 Hz, this corresponds to about 14 samplepoints, which may be coarser than desired for analysis. In some suchcircumstances, the processing equipment may interpolate the data togenerate additional data points in between the sampled data points. Theinterpolation may be linear, spline, or any other suitable interpolationtechnique. The interpolated data may include an increased number of datapoints, having a reduced spacing interval. Interpolation may, in someinstances, aid implementation of one or more Qualification Techniques.In some embodiments, interpolation may be applied during signalconditioning. For example, interpolation may be applied before or afterapplying any of the techniques described in the context of FIGS. 42-61.

It will be understood that the Qualification Techniques disclosed hereinare merely illustrative and any suitable variations may be implementedin accordance with the present disclosure. In some embodiments, theQualification Techniques may be performed on signals other thancross-correlation signals. For example, the Qualification Techniques maybe performed on any suitable physiological signal or any suitable signalderived thereof, such as a raw intensity signal, a conditioned intensitysignal, and an autocorrelation signal. In some embodiments, it may bedesired that the signal used in the Qualification Techniques includes aperiodic component that corresponds to a physiological rate. It willalso be understood that although plots of graphical representations ofdata are shown for illustration, the disclosed techniques need notrequire plotting data or generating graphical representations.

It will be understood that the templates used in the QualificationTechniques need not be scaled. In some embodiments, a library oftemplates may be stored in memory and accessible by the processingequipment. For each template, the library may store a complete range ofdesired template lengths (e.g., lengths corresponding to the range of20-300 BPM, with a resolution of 1 BPM). Therefore, instead of scaling atemplate, the processing equipment may select a template with a desiredlength from the library. Regardless of how the template is selected orgenerated, the template may be referred to herein as a “referencewaveform.”

In some embodiments, the processing equipment may calculate aphysiological rate based on a correlation lag value (e.g., as shown bysteps 422 and 424 of flow diagram 400 of FIG. 4), and manage posting ofthe calculated rate. For example, a physiological rate may be calculatedby qualifying a correlation lag value, and determining the rate that hasa period corresponding to the calculated lag value (e.g., for acalculated correlation lag value of 1 second, the associatedphysiological rate would be 60 BPM). The processing equipment may alsomanage posting when a correlation lag value is not identified or when acorrelation lag value is disqualified. Posting the rate may includestoring the calculated value, displaying the calculated value to a user,any other suitable output functions, or any combination thereof. Someaspects of managing rate calculation and posting are described in thecontext of FIGS. 127-128, for example.

FIG. 127 is a flow diagram 12700 of illustrative steps for managingposting a value indicative of a physiological parameter, in accordancewith some embodiments of the present disclosure.

Step 12702 may include processing equipment receiving physiological datafrom a physiological sensor, memory, any other suitable source, or anycombination thereof. For example, referring to system 300 of FIG. 3, theprocessing equipment may receive a window of physiological data frominput signal generator 310. Sensor 318 of input signal generator 310 maybe coupled to a subject, and may detect physiological activity such as,for example, RED and/or IR light attenuation by tissue, using aphotodetector. In some embodiments, physiological signals generated byinput signal generator 310 may be stored in memory (e.g., RAM 54 of FIG.2, QSM 72 and/or other suitable memory) after being pre-processed bypre-processor 320. In such cases, step 12702 may include recalling datafrom the memory for further processing.

Step 12704 may include processing equipment filtering the physiologicaldata of step 12702 with an adjustable filter to generate filtered data.The adjustable filter may include one or more adjustable settings. Forexample, the filter may be a bandpass filter having an adjustable centerfrequency and an adjustable passband. In a further example, the filtermay be either a lowpass filter or a highpass filter, having anadjustable cutoff frequency. In some embodiments, the filter may beadjusted based on a previously calculated metric of step 12708.

Step 12706 may include processing equipment performing calculations overtime on the filtered data to determine a value indicative of aphysiological parameter, where some of the calculations are qualifiedand some calculations are disqualified. For example, the processingequipment may perform a rate calculation over time based on thephysiological data, for which some rate values may be qualified andposted while others are disqualified and accordingly not posted.

Step 12708 may include processing equipment determining a dropout metricbased on the physiological data, where the dropout metric is used todetermine whether to output a value indicative of a physiologicalparameter when a sufficient value is not determined at step 12706. Themetric may be based on one or more noise metrics, one or more noisequalification metrics, calculation history (e.g., the number ofconsecutive calculations that did not result in sufficient values),current algorithm mode, or a combination thereof.

Step 12710 may include processing equipment outputting a value based onone or more previously calculated values when a current calculation isdisqualified and a criterion is satisfied. In some embodiments, theprocessing equipment may maintain one or more counters. For example, theprocessing equipment may maintain a dropout counter that counts thenumber of dropout events (e.g., rate disqualifications) of the ratealgorithm. The processing equipment may compare the dropout countervalue to a predetermined threshold, or an adjustable threshold which maydepend on a metric value (e.g., the metric value determined at step12708). For example, the processing equipment may determine a thresholdbased on the noise metric, and output a calculated rate value based onone or more previously calculated rate values based on a comparison ofthe dropout counter value to the threshold.

In some embodiments, the processing equipment may manage rate posting tomitigate disruptions in the posted rate value due to noise. For example,when the noise level in the physiological data is relatively high andsufficient values are not being calculated, the rate algorithm maycontinue posting previous values to disregard the noise. However, whenthe noise level in the physiological is relatively low, the ratealgorithm may dropout more quickly because the rate algorithm should beable to calculate a rate with higher accuracy. This behavior by the ratealgorithm may be especially useful when the bandpass filter is filteringat the wrong rate (e.g., the central frequency is tuned to noise), andit is desirable to drop out and start posting values again correspondingto the correct rate. In some embodiments, this behavior may beimplemented by using a dropout counter that counts up each time aninsufficient value is calculated (and cleared or decremented every timea sufficient value is calculated). The counter can, for example, becompared against a threshold that is based on the dropout metric.Alternatively, the threshold may be fixed and the dropout metric may beused to scale or otherwise modify the dropout counter value.

FIG. 128 is a block diagram 12800 of illustrative modes of a ratealgorithm, in accordance with some embodiments of the presentdisclosure. Modes 12802, 12804, 12806, and 12808 may be implementedusing hardware modules, software modules, modes of a single softwarealgorithm, a single processing unit, or any combination thereof. In someembodiments, for example, the rate algorithm may implement any of thetechniques described in the context of FIG. 127 as part of one or moremodes.

Mode 12802 is an algorithm initialization mode. Mode 12802 may beperformed during, for example, startup when a rate has not yet beencalculated or after the rate algorithm starts over after failing tocalculate a rate. Mode 12802 may set a de-trend parameter based on apredetermined setting or a de-trend metric determined based onphysiological data. When the de-trend parameter has been set, the ratealgorithm may transition from mode 12802 to mode 12804.

Mode 12804 is a first rate posting mode of the rate algorithm. Mode12804 may be performed as long as there is no unqualified lag value orgain change event. Mode 12804 may use a broadly set bandpass filter tofilter physiological data, or no bandpass filter. Mode 12804 maycalculate rates for qualified lag values, including the first qualifiedlag value. Mode 12804 may post each of the calculated rates. In someembodiments, each of the calculated rates may be added to a rate filterto smooth the displayed rate, except for the first rate. If adisqualification occurs, or a gain change event occurs, mode 12804 maytransition back to mode 12802. After “X” consecutive qualified lags, andno gain change event, mode 12804 may transition to mode 12806. “X” maybe a fixed number or it may be adjustable based on one or more criteriaor metrics. The transition to block 12806 may include selecting andstoring narrowly set bandpass filter coefficients based on the lastqualified lag value, posting the rate associated with the last qualifiedlag, and clearing the rate filter and any rate value buffers.

Mode 12806 is a second rate posting mode. Mode 12806 may be performed aslong as there is no unqualified lag value, other than the firstcalculated value, or gain change event. Mode 12806 may use a narrowlyset bandpass filter to filter physiological data. Mode 12806 may use arate filter based on the qualified lag values. Mode 12806 may calculaterates for qualified lag values, including the first qualified lag value,and post the filtered rate. If the first lag value calculated by mode12806 is disqualified, or if a gain change event occurs, mode 12806 maytransition to mode 12802 to reinitialize. If a disqualification occursafter the first rate calculation of mode 12806, or a gain change eventoccurs, mode 12804 may transition to mode 12808 and post the previouslyposted rate. In some embodiments, the previous rate information may beretained when the rate algorithm transitions from mode 12804 to mode12806. If the rate information is retained, the rate algorithm need notreinitialize when the first lag value is disqualified in mode 12806.Instead, the rate algorithm may transition to mode 12808 and post thepreviously posted rate.

Mode 12808 is a third rate posting mode. Mode 12808 may be performeduntil a lag value is qualified, and no gain change event occurs. Mode12808 may use a narrowly set bandpass filter to filter physiologicaldata. Mode 12808 may hold the rate filter based on the settings andvalues from mode 12806. Mode 12808 attempts to identify and qualify lagvalues, but posts the last rate from mode 12806, until a new lag valueis identified and qualified. If “Y” consecutive lag values aredisqualified by mode 12808, mode 12808 may transition to mode 12802 toreinitialize. “Y” may be a fixed number or it may be adjustable based onone or more criteria or metrics. The transition back to block 12802 mayinclude posting the previously posted rate, clearing the rate filter,clearing any rate value buffers, and clearing the current de-trendparameter. If a lag value calculated by mode 12808 is qualified, and nogain change event occurs, mode 12808 may transition back to mode 12806.The transition back to mode 12806 may include adding the rate associatedwith the qualified lag value to the rate filter, and posting the rateassociated with the qualified lag value.

FIG. 129 is a flow diagram 12900 of illustrative steps for calculatingand posting a physiological rate, in accordance with some embodiments ofthe present disclosure.

Step 12902 may include processing equipment initializing the ratealgorithm. Initialization may include initializing the buffer (e.g.,padding the buffer with initialization values), setting a Dropout StatusFlag to “True,” any other suitable processing functions, or anycombination thereof. In some embodiments, the processing equipment mayimplement any of the illustrative techniques of step 402 of flow diagram400 of FIG. 4, the illustrative techniques of flow diagram 500 of FIG.5, the illustrative techniques of flow diagram 900 of FIG. 9, or anycombination thereof.

After initialization at step 12902, the rate algorithm may perform “persample processing” at steps 12904-12912, to fill the buffer with datasamples from a physiological signal. Step 12904 may include processingequipment adding a current data sample to the buffer. Step 12906 mayinclude determining whether a gain change event, pulse lost, or sensoroff event, has occurred based on status flags 12908. If an event hasoccurred, the processing equipment may manage the event at step 12910.For example, managing the event may include holding a current state ofthe buffer, and not adding new data until the event has passes. In afurther example, managing the event may include stopping the ratealgorithm, and restarting when a sensor off event has ended. In afurther example, managing the event may include performing any of theillustrative steps of flow diagram 800 of FIG. 8. If no event hasoccurred, the processing equipment may determine whether one second haspassed since initialization or the last rate calculation. If one secondhas not passed, as determined at step 12912, the processing equipmentmay continue adding data samples to the buffer until a second haspassed. If one second has passed, as determined at step 12912, theprocessing equipment may transition to “per second processing” at steps12914-12938. It will be understood that the one second check in step12912 is merely illustrative and any suitable calculation interval maybe used.

Step 12914 may include processing equipment performing a firstpre-processing of the physiological data (e.g., see FIG. 131 for furtherdetails). The first pre-processing may include de-trending, smoothing,any other suitable signal conditioning, or any combination thereof. Forexample, any of the illustrative techniques described in the context ofFIGS. 42-61 may be used at step 12914.

Step 12916 may include processing equipment determining whether a ModeStatus Flag value has changed, a Dropout Status Flag value has changed,or both. If either status flag has a value of “True,” the processingequipment may clear the rate filter and data buffer at step 12928. Theprocessing equipment may then determine de-trending settings at step12930 (e.g., see FIG. 130 for further details), and set switches basedon switch setting flags at step 12932, before proceeding to step 12920.If neither status flag has a value of “True,” the processing equipmentmay maintain the previous switch settings at step 12918.

Step 12920 may include processing equipment performing a secondpre-processing of the physiological data (e.g., see FIG. 132 for furtherdetails). The second pre-processing may include de-trending, applying aderivative limiter, applying a bandpass filter, performing meansubtraction, applying an FIR filter, any other suitable signalconditioning, or any combination thereof. For example, any of theillustrative techniques described in the context of FIGS. 42-61 may beused at step 12920.

Step 12922 may include processing equipment calculating a regressioncorrelation using the pre-processed window of data of the buffer. Theprocessing equipment may determine a correlation sequence at step 12922.Step 12924 may include the processing equipment determining acorrelation lag value corresponding to a peak or maximum in thecorrelation sequence of step 12922. The processing equipment may, forexample, apply any of the illustrative techniques described in thecontext of FIGS. 62-85, to calculate the regression correlation anddetermine the correlation lag value.

Step 12926 may include processing equipment applying one or moreQualification Techniques (e.g., see FIG. 133 for further details) to thecorrelation lag value of step 12924. The processing equipment maydetermine whether the one or more qualification tests are passed (e.g.,the correlation lag value is qualified) at step 12934. If thecorrelation lag value is disqualified, the processing equipment maymanage rate posting and prepare for the next iteration of the ratealgorithm at step 12938 (e.g., see FIG. 134 for further details). If thecorrelation lag value is qualified, the processing equipment maycalculate the instantaneous rate, manage posting, and prepare for thenext iteration of the rate algorithm at step 12936 (e.g., see FIG. 135for further details). The processing equipment may then continue to adddata samples to the buffer and repeat per sample processing and persecond processing as appropriate.

FIG. 130 is a flow diagram of illustrative steps for determiningde-trending settings and qualification settings, corresponding to step12930 of flow diagram 12900 of FIG. 129, in accordance with someembodiments of the present disclosure. Step 13002 may include processingequipment determining whether a Dropout Status Flag value is “True.” Ifthe Dropout Status Flag value is not “True,” the processing equipmentmay proceed to step 12932 of flow diagram 12900 of FIG. 129. If theDropout Status Flag value is “True,” the processing equipment maycalculate a de-trend metric at step 13004 based on one or more noisemetrics 13006 (e.g., which may be calculated in a separate module). Ifthe de-trend metric is greater than a predetermined value “X,” asdetermined at step 13008, the processing equipment may set the De-trendStatus Flag to “High,” while if the de-trend metric less than “X,” theprocessing equipment may set the De-trend Status Flag to “Low,” as shownrespectively by steps 13012 and 13010. Step 13014 may include theprocessing equipment setting the Dropout Status Flag to “False,” settingthe Mode Status Flag to “Mode 1,” and setting a qualification thresholdto “setting 1” (e.g., see FIG. 133 for further details). Step 13016 mayinclude the processing equipment setting a Rate History counter to zero.The processing equipment may proceed to step 12932 of flow diagram 12900of FIG. 129.

FIG. 131 is a flow diagram 13100 of illustrative steps forpre-processing physiological data, corresponding to step 12914 of flowdiagram 12900 of FIG. 129, in accordance with some embodiments of thepresent disclosure. Step 3102 may include processing equipment applyingquadratic and cubic de-trending to the physiological data as described,for example, in flow diagram 4300 of FIG. 43. Step 13104 may include theprocessing equipment applying a normalization technique to thepre-processed data from step 13102 such as, for example, that describedin flow diagram 5600 of FIG. 56.

FIG. 132 is a flow diagram 13200 of illustrative steps for furtherpre-processing physiological data, corresponding to step 12920 of flowdiagram 12900 of FIG. 129, in accordance with some embodiments of thepresent disclosure. Step 13202 may include processing equipment applyinga derivative limiter to the physiological data from step 12918 of flowdiagram 12900 of FIG. 129. The processing equipment may apply thederivative limiter using, for example, any of the illustrativetechniques discussed in the context of FIGS. 50-55. Step 13204 mayinclude processing equipment applying quadratic and cubic de-trending tothe derivative limited physiological data as described, for example, inflow diagram 4300 of FIG. 43. Step 13206 may include processingequipment applying a broadly-set bandpass filter to the physiologicaldata from step 13204. The bandpass filter may be set to pass theexpected frequency range of physiological rates. Step 13208 may includethe processing equipment applying a normalization technique to thebandpass filtered data from step 13206 such as, for example, thatdescribed in flow diagram 5600 of FIG. 56. Step 13210 may include theprocessing equipment applying a normalization technique to the data fromstep 13208 such as, for example, that described in flow diagram 5800 ofFIG. 58. Step 13212 may include the processing equipment applying anormalization technique to the data from step 13210 such as, forexample, that described in flow diagram 5600 of FIG. 56. The threenormalization techniques applied at steps 13208, 13210, and 13212 mayaid in further processing of the data by reducing baseline shifts andamplitude variations in the physiological data. The processing equipmentmay apply a filter switch at step 13214, in which “setting a”corresponds to no bandpass filter application, and “setting b”corresponds to application of a bandpass filter and mean subtraction.The bandpass filter may have one or more filter coefficients 13218,which may be based on a calculated rate, a noise metric, an operatingmode, any other criterion, or any combination thereof. Whether thetracking bandpass filter is applied or not, the processing equipment maythen proceed to step 13220, and clear the tracking bandpass filtercoefficients from memory (e.g., future filter coefficients may bedetermined on subsequent calculated rates). The processing equipment mayapply a de-trending switch at step 13222, in which “setting a”corresponds to a De-trend Status Flag value of “Low,” and “setting b”corresponds to a De-trend Status Flag value of “High.” If the processingequipment selects setting b at step 13222, the processing equipment maythen proceed to step 13224. Step 13224 may include the processingequipment applying a FIR filter such as that described in flow diagram6000 of FIG. 60 (e.g., using a weighted sum of the physiological dataand a difference signal derived thereof), and also applying a meansubtraction. The processing equipment may then proceed to step 12922 offlow diagram 12900 of FIG. 129.

FIG. 133 is a flow diagram 13300 of illustrative steps for qualifying ordisqualifying a correlation lag value, corresponding to step 12926 offlow diagram 12900 of FIG. 129, in accordance with some embodiments ofthe present disclosure. Step 13302 may include processing equipmentdetermining whether a correlation lag value was identified at step 12924of flow diagram 12900 of FIG. 129. If no correlation lag value wasidentified, the processing equipment may determine that qualificationhas failed at step 13330. If a correlation lag value was identified, theprocessing equipment may proceed to step 13304 and determine one or morelag metrics. Step 13306 may include the processing equipment calculatinga dropout limit using, for example, a single equation, one or more lagmetrics, one or more noise metrics, a correlation lag value, any othersuitable information, or any combination thereof. In some embodiments,the lag metrics and drop out limit may be determined by processing oneor more versions of the processed data. For example, the processed datacan be the data at the end of preprocessing stage 1, before the firstswitch in preprocessing stage 2, at the end of stage 2, at any otherintermediate stage or after any other suitable preprocessing. In someembodiments, the processing equipment may determine different metricsusing different processed data. The processing equipment may apply aqualification switch at step 13308, in which “setting a” corresponds toa first qualification threshold, “setting b” corresponds to a secondqualification threshold, through “setting c” which corresponds to athird qualification threshold, although any suitable number of settingsmay be used. For “setting a,” the processing equipment may perform asequence of qualification tests at steps 13310, 13312, through 13314. Ifany of the qualifications tests for “setting a” fail, the processingequipment may determine that qualification has failed at step 13330. For“setting b,” the processing equipment may perform a sequence ofqualification tests at steps 13316, 13318, through 13320. If any of thequalifications tests for “setting b” fail, the processing equipment maydetermine that qualification has failed at step 13330. For “setting c,”the processing equipment may perform a sequence of qualification testsat steps 133122, 13324, through 13326. If any of the qualificationstests for “setting c” fail, the processing equipment may determine thatqualification has failed at step 13330. For any setting, if eachqualification test is passed, the processing equipment may determinethat qualification has passed at step 13328. Steps 13310-13326 mayinclude any suitable Qualification Techniques, having any suitablequalification settings. The processing equipment may proceed to step12934 of flow diagram 12900 of FIG. 129 when the qualification tests arecomplete. The techniques of flow diagram 13300 are merely illustrative,and any single qualification test, or combination of qualificationtests, may be applied using a calculated correlation lag value. In someembodiments, different qualification tests may be performed for eachswitch setting. For example, in some embodiments, one or morequalification tests may be run in parallel, series, or a combinationthereof. In a further example, one or more neural network techniques,based on one or more metric values or other qualification results, maybe used.

FIG. 134 is a flow diagram 13400 of illustrative steps for managingalgorithm settings when a correlation lag value is disqualified,corresponding to step 12938 of flow diagram 12900 of FIG. 129, inaccordance with some embodiments of the present disclosure. Theprocessing equipment may determine which Mode the rate algorithm isoperating in (e.g., the Mode Status Flag value) at steps 13402 and13408. If the rate algorithm is operating in Mode 1, the processingequipment does not post a rate value, as shown by step 13404. Theprocessing equipment may then set the Dropout Status Flag to “True” atstep 13406. If the rate algorithm is operating in either of Modes 2 or3, the processing equipment may determine whether the rate history(e.g., the iteration value with 1 being the first calculated rate inMode 2, and Y being the first calculated rate in Mode 3) is either 1 orY, which may indicate that the current rate is the first calculated ratein Mode 2 or Mode 3, respectively. For example, the value Y may be 9seconds (i.e., 9 iterations for per second processing) in someimplementations. If the rate history is 1 or Y, then the processingequipment may proceed to dropout, by not posting the rate at step 13424,and setting the Dropout Status Flag to “True” at step 13426. If the ratehistory is not 1 or Y, the processing equipment may compare a dropoutcounter to a dropout limit at step 13412. If the dropout counter exceedsthe dropout limit, the processing equipment may proceed to dropout, byposting the previous rate at step 13420, and setting the Dropout StatusFlag to “True” at step 13422. If the dropout counter does not exceed thedropout limit, the processing equipment may proceed to age and hold, byposting the previous rate at step 13414, incrementing the dropoutcounter (e.g., to indicate the qualification failure) at step 13416, andincrementing the rate history counter at step 13418. The processingequipment may proceed to per sample processing after completing therelevant illustrative steps of flow diagram 12900 of FIG. 129.

FIG. 135 is a flow diagram 13500 of illustrative steps for managingalgorithm settings when a correlation lag value is qualified,corresponding to step 12936 of flow diagram 12900 of FIG. 129, inaccordance with some embodiments of the present disclosure. Step 13502may include processing equipment calculating an instantaneous rate valuebased on the qualified correlation lag value. For example, the rate maybe determined as having a period equal to the qualified correlation lagvalue. Step 13504 may include processing equipment incrementing the ratehistory counter. The processing equipment may determine which Mode therate algorithm is operating in (e.g., the Mode Status Flag value) atsteps 13506, 13516, and 13530. If the rate algorithm is operating inMode 1, as determined at step 13506, the processing equipment does notpost a new rate value, as shown by step 13508. The processing equipmentmay then set the dropout counter value to zero at step 13510, clear thedropout counter limit at step 13512, and set the Mode Status Flag valueto “Mode 2” and the qualification threshold to “setting 2” at step13514. If the rate algorithm is operating in Mode 2, as determined atstep 13516, the processing equipment may filter and post the calculatedrate at step 13518. In some embodiments, the processing equipment maynot add the first calculated rate to the filter. The processingequipment may then decrement the dropout counter value by 2, but notbelow zero, at step 13520. The processing equipment may determine atstep 13522 if the rate history counter is greater than or equal to avalue “X,” which is the rate counter limit to transition to Mode 3. Ifthe rate history counter is less than “X,” the processing equipment mayproceed to per sample processing of flow diagram 12900 of FIG. 129. Ifthe rate history counter is greater than or equal to “X,” the processingequipment may clear the dropout counter limit at step 13524, set theMode Status Flag value to “Mode 3” and set the qualification thresholdto “setting 3” at step 13526, and select bandpass filter coefficientsbased on the posted rate at step 13528 before proceeding to per sampleprocessing of flow diagram 12900 of FIG. 129. If the rate algorithm isoperating in Mode 3, as determined at step 13530, the processingequipment may determine if the rate history counter is equal to a value“Y,” which is the first rate calculation performed in Mode 3, at step13532. If the rate history counter is equal to “Y,” the processingequipment may post the instantaneous rate but not add the rate to therate filter, as shown by step 13534. If the rate history counter is notequal to “Y,” the processing equipment may filter and post thecalculated rate at step 13536. The processing equipment may thendecrement the dropout counter value by 2, but not below zero, at step13538. The processing equipment may then select bandpass filtercoefficients based on the posted rate at step 13540 before proceeding toper sample processing of flow diagram 12900 of FIG. 129.

FIG. 136 is a flow diagram 13600 of illustrative steps for determining aphysiological parameter using more than one algorithm mode in parallel,in accordance with some embodiments of the present disclosure.

Step 13602 may include processing equipment determining a physiologicalparameter using rate algorithm Mode 1. Step 13604 may include processingequipment determining a physiological parameter using rate algorithmMode 2. Step 13606 may include processing equipment determining aphysiological parameter using rate algorithm Mode N, which may be anysuitable integer greater than one. Step 13608 may include determining aphysiological parameter for outputting based on one or more of the ratealgorithm Modes 1-N. The foregoing illustrative embodiments, shown inFIGS. 128-135, show a sequential transition between Modes over timebased on rate qualifications and algorithm settings. In someembodiments, one or more Modes of the rate algorithm may be operated inparallel (e.g., simultaneously or sequentially on the same physiologicaldata). For example, the rate algorithm may operate in Model for thefirst iteration, and once a correlation lag value is qualified, the ratealgorithm may operate in Modes 1 and 2 in parallel. Further, oncesufficient confidence is obtained, Mode 3 operation can be initiated,and the rate algorithm may operate in Modes 1, 2, and 3 in parallel. Insome embodiments, more than three Modes may be used. For example, therate algorithm may operate in two variations of Mode 3, using both highand low de-trending, respectively, or other setting variations. In someembodiments, the history of metric values (e.g., noise metrics,de-trending metrics, qualification metrics, or other metrics) andphysiological parameter values from operation in the different Modes maybe stored in memory and analyzed to determine which physiologicalparameter value should be outputted. For example, operation in aparticular Mode using a tracking bandpass filter may start to tracknoise in the physiological data rather than the signal componentcorresponding to the true physiological rate. By analyzing thephysiological data using multiple Modes (e.g., including Modes that donot use the tracking bandpass filter), the processing equipment maydetermine that the tracking filter mode is likely wrong. For example, ifa calculated noise metric value is low and a majority of Modes indicatea calculated rate different than the calculated rate of the trackingbandpass filter Mode, then the processing equipment may ignore or resetthe tracking bandpass filter Mode.

The foregoing is merely illustrative of the principles of thisdisclosure and various modifications may be made by those skilled in theart without departing from the scope of this disclosure. The abovedescribed embodiments are presented for purposes of illustration and notof limitation. The present disclosure also can take many forms otherthan those explicitly described herein. Accordingly, it is emphasizedthat this disclosure is not limited to the explicitly disclosed methods,systems, and apparatuses, but is intended to include variations to andmodifications thereof which are within the spirit of the followingclaims.

What is claimed is:
 1. A subject monitoring pulse oximetry system fordetermining physiological information of a subject, comprising: a pulseoximetry sensor configured to detect light attenuated by the subject andgenerate a photoplethysmographic (PPG) signal based on the detectedattenuated light; and a pulse oximeter coupled to the pulse oximetrysensor, wherein the pulse oximeter is configured to: store a ratealgorithm; determine a skew metric based on the PPG signal; determine analgorithm setting based on a reference relationship between thedetermined skew metric and a value indicative of a physiological rate;apply the algorithm setting to the rate algorithm stored by the pulseoximeter; and determine the physiological information of the subjectusing the rate algorithm and the PPG signal, wherein the physiologicalinformation comprises pulse rate of the subject.
 2. The system of claim1, wherein the reference relationship comprises a look-up table, whereinthe pulse oximeter is further configured to: reference the look-up tableusing the skew metric as an input; and determine the value indicative ofphysiological rate based on the look-up table.
 3. The system of claim 1,wherein the reference relationship comprises a function, wherein thepulse oximeter is further configured to: reference the function usingthe skew metric as an input; and determine the value indicative ofphysiological rate based on the function.
 4. The system of claim 1,wherein the algorithm setting comprises a central frequency of abandpass filter, and wherein the pulse oximeter is configured todetermine the central frequency to be substantially equal to the valueindicative of the physiological rate.
 5. The system of claim 1, whereinthe algorithm setting comprises a cutoff frequency of a lowpass filter,and wherein the pulse oximeter is configured to determine the cutofffrequency to be greater than the value indicative of the physiologicalrate.
 6. The system of claim 1, wherein the algorithm setting comprisesa cutoff frequency of a highpass filter, and wherein the pulse oximeteris configured to determine the cutoff frequency to be less than thevalue indicative of the physiological rate.
 7. The system of claim 1,wherein the algorithm setting comprises a finite impulse responsefilter, and wherein the pulse oximeter is configured to determinewhether to apply the finite impulse response filter based on the skewmetric.
 8. A pulse oximetry processing module for determiningphysiological information of a subject, wherein the pulse oximetryprocessing module comprises a non-transitory computer readable mediumhaving instructions stored thereon, wherein the instructions comprise arate algorithm, and wherein the instructions are configured to instructthe pulse oximetry processing module to: receive a photoplethysmographic(PPG) signal derived from a pulse oximetry sensor output, wherein thepulse oximetry sensor detects light attenuated by the subject andgenerates the PPG signal based on the detected attenuated light;determine a skew metric based on the PPG signal; determine an algorithmsetting based on a reference relationship between the determined skewmetric and a value indicative of a physiological rate; apply thealgorithm setting to the rate algorithm stored by the pulse oximetryprocessing module; and determine the physiological information of thesubject using the rate algorithm and the PPG signal, wherein thephysiological information comprises pulse rate of the subject.
 9. Thepulse oximetry processing module of claim 8, wherein the referencerelationship comprises a look-up table, wherein the instructions arefurther configured to instruct the pulse oximetry processing module to:reference the look-up table using the skew metric as an input; anddetermine the value indicative of physiological rate based on thelook-up table.
 10. The pulse oximetry processing module of claim 8,wherein the reference relationship comprises a function, wherein theinstructions are further configured to instruct the pulse oximetryprocessing module to: reference the function using the skew metric as aninput; and determine the value indicative of physiological rate based onthe function.
 11. The pulse oximetry processing module of claim 8,wherein the algorithm setting comprises a central frequency of abandpass filter, and wherein the instructions are configured to instructthe pulse oximetry processing module to determine the central frequencyto be substantially equal to the value indicative of the physiologicalrate.
 12. The pulse oximetry processing module of claim 8, wherein thealgorithm setting comprises a cutoff frequency of a lowpass filter, andwherein the instructions are configured to instruct the pulse oximetryprocessing module to determine the cutoff frequency to be greater thanthe value indicative of the physiological rate.
 13. The pulse oximetryprocessing module of claim 8, wherein the algorithm setting comprises acutoff frequency of a highpass filter, and wherein the instructions areconfigured to instruct the pulse oximetry processing module to determinethe cutoff frequency to be less than the value indicative of thephysiological rate.
 14. The pulse oximetry processing module of claim 8,wherein the algorithm setting comprises a finite impulse responsefilter, and wherein the instructions are configured to instruct thepulse oximetry processing module to determine whether to apply thefinite impulse response filter based on the skew metric.
 15. A methodfor determining physiological information of a subject, comprising:storing, using a pulse oximeter, a rate algorithm; receiving, using thepulse oximeter, a photoplethysmographic (PPG) signal derived from apulse oximetry sensor output, wherein the pulse oximetry sensor detectslight attenuated by the subject and generates the PPG signal based onthe detected attenuated light; determining, using the pulse oximeter, askew metric based on the PPG signal; determining, using the pulseoximeter, an algorithm setting based on a reference relationship betweenthe determined skew metric and a value indicative of a physiologicalrate; applying, using the pulse oximeter, the algorithm setting to thestored rate algorithm; and determining, using the pulse oximeter, thephysiological information of the subject using the rate algorithm andthe PPG signal, wherein the physiological information comprises pulserate of the subject.
 16. The method of claim 15, wherein the referencerelationship comprises a look-up table, the method further comprising:referencing the look-up table using the skew metric as an input; anddetermining the value indicative of physiological rate based on thelook-up table.
 17. The method of claim 15, wherein the referencerelationship comprises a function, the method further comprising:referencing the function using the skew metric as an input; anddetermining the value indicative of physiological rate based on thefunction.
 18. The method of claim 15, wherein the algorithm settingcomprises a central frequency of a bandpass filter, and whereindetermining the algorithm setting comprises determining the centralfrequency to be substantially equal to the value indicative of thephysiological rate.
 19. The method of claim 15, wherein the algorithmsetting comprises a cutoff frequency of a lowpass filter, and whereindetermining the algorithm setting comprises determining the cutofffrequency to be greater than the value indicative of the physiologicalrate.
 20. The method of claim 15, wherein the algorithm settingcomprises a cutoff frequency of a highpass filter, and whereindetermining the algorithm setting comprises determining the cutofffrequency to be less than the value indicative of the physiologicalrate.
 21. The method of claim 15, wherein the algorithm settingcomprises a finite impulse response filter, and wherein determining thealgorithm setting comprises determining whether to apply the finiteimpulse response filter based on the skew metric.
 22. A non-transitorycomputer-readable medium for use in determining physiologicalinformation of a subject, the computer-readable medium comprising:computer program instructions recorded thereon for causing a pulseoximeter storing a rate algorithm to: receive a photoplethysmographic(PPG) signal derived from a pulse oximetry sensor output, wherein thepulse oximetry sensor detects light attenuated by the subject andgenerates the PPG signal based on the detected attenuated light;determine a skew metric based on the PPG signal; determine an algorithmsetting based on a reference relationship between the determined skewmetric and a value indicative of a physiological rate; apply thealgorithm setting to the stored rate algorithm; and determine thephysiological information of the subject using the rate algorithm andthe PPG signal, wherein the physiological information comprises pulserate of the subject.
 23. The non-transitory computer-readable medium ofclaim 22, wherein the reference relationship comprises a look-up table,the computer-readable medium comprising further computer programinstructions recorded thereon for causing the pulse oximeter to:reference the look-up table using the skew metric as an input; anddetermine the value indicative of physiological rate based on thelook-up table.
 24. The non-transitory computer-readable medium of claim22, wherein the reference relationship comprises a function, thecomputer-readable medium comprising further computer programinstructions recorded thereon for causing a pulse oximeter to: referencethe function using the skew metric as an input; and determine the valueindicative of physiological rate based on the function.
 25. Thenon-transitory computer-readable medium of claim 22, wherein thealgorithm setting comprises a central frequency of a bandpass filter,and wherein determining the algorithm setting comprises determining thecentral frequency to be substantially equal to the value indicative ofthe physiological rate.
 26. The non-transitory computer-readable mediumof claim 22, wherein the algorithm setting comprises a cutoff frequencyof a lowpass filter, and wherein determining the algorithm settingcomprises determining the cutoff frequency to be greater than the valueindicative of the physiological rate.
 27. The non-transitorycomputer-readable medium of claim 22, wherein the algorithm settingcomprises a cutoff frequency of a highpass filter, and whereindetermining the algorithm setting comprises determining the cutofffrequency to be less than the value indicative of the physiologicalrate.
 28. The non-transitory computer-readable medium of claim 22,wherein the algorithm setting comprises a finite impulse responsefilter, and wherein determining the algorithm setting comprisesdetermining whether to apply the finite impulse response filter based onthe skew metric.